面向产品持续质量控制的数据挖掘技术与应用研究
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摘要
知识是制造企业最有价值的资产。数据挖掘能够从大量的各种业务数据中提炼出有价值的知识,从而极大促进了制造技术和制造模式的发展。关联规则挖掘是一种最重要的数据挖掘技术之一,它可以有效地发现数据项之间的关联,并且规则的表达形式简洁,易于理解和解释,因此关联规则挖掘算法的研究具有重要的理论意义和广阔的应用前景,一直是数据挖掘领域研究的热点。本文对关联规则挖掘关键技术及其在产品持续质量改善中的应用做了深入的研究,主要的创新性工作包括：
(1)为了构建条件FP-tree,FP-growth算法必须扫描数据库两次,这极大制约了它的应用。针对这一局限性,本文提出一种新颖的FP阵列技术,直接从FP阵列得到频繁项的计数,从而极大减少了遍历FP-tree的需要。本文将FP-tree数据结构与FP阵列有效地结合起来,分别提出了挖掘频繁项集和闭频繁项集的新算法。实验评测表明这两种算法在运行时间、内存消耗和可扩展性方面都具有稳定优良的性能,尤其对于稀疏数据库。
(2)Apriori算法和FP-growth算法都是以批处理方式处理所有事务,无法满足动态更新关联规则的需要。本文在FUFP算法的基础上提出了一种基于次频繁项的改进算法,在算法中引入两个支持度阈值：阈值上限和阈值下限。如果处理的新事务数没有达到一定的值(由两个支持度阈值和数据库的规模决定),该算法就不需要重新扫描原数据库,从而提高了关联规则更新的效率。实验评测表明数据库的规模越大,算法的性能优势越明显。
(3)传统关联规则挖掘算法不能同时处理多种类型的数据,无法适应多样性客户需求数据挖掘的需要。针对这一局限性,本文首先给出了各种数据类型的定义以及挖掘的规则模式的定义,提出用相似度统计项目的支持度计数,然后提出一种基于模糊集的新方法以统一的方式处理各种数据类型,最后提出一种基于Apriori的模糊关联规则挖掘算法,并将其应用到电动自行车问卷调查数据的关联分析。
(4)以上述研究工作为基础,本文开发了一个产品持续质量改善信息系统(ARMS),其目标是以低成本、低资源消耗为代价生产高质量产品,提高客户的满意度。ARMS由三个模块组成：流程数据集成模块、关联规则挖掘模块和关联规则优化模块。ARMS系统采用基于XML的流程质量语言将各有关部门的流程数据集成到中央数据仓库,在此基础上采用本文提出的新算法发现不同部门的流程参数组合与产品质量特性之间的关联规则,再运用遗传算法优化这些规则,从而帮助流程工程师调整流程参数的设置以持续提高产品的质量。图92幅,表19个,参考文献202篇。
Knowledge is the most valuable assets of a manufacturing enterprise. Data mining can extract valuable knowledge from all kinds of manufacturing data, which has promoted enormously the development of manufacturing technology and manufacturing mode. Association rules is one of the most important data mining technologies which can effectively find the relationship between data items. And the expressions of association rules are concise and easy to understand and explain. So association rules algorithm research has important theoretical significance and broad application prospect which has been a hot research field of data mining. In this paper, the key technologies of association rules and their application in product continual quality improvement have been studied deeply. The main innovation work is as follows:
(1) For generating conditional FP-tree, FP-growth algorithm need scanning database twice. Thus FP-growth algorithm can't adapt to the characteristics of data in dynamic real-time database. Aiming at the limitations, this paper presents a novel FP array technology. The counts of frequent items are obtained directly from FP array, thus the first scan is omitted. An improved frequent itemsets mining algorithm and a closed frequent itemsets mining algorithm are presented which use the FP-tree data structure in combination with the FP array technology. Experimental evaluations show that the two algorithms have stable superior performance in running time, memory consumption and scalability aspects especially for the sparse database.
(2) Apriori and FP-growth algorithm process all transactions in a batch way which can't adapt to the need to update association rules dynamically. This paper presents the concept of pre-frequent itemsets. Through an upper minimum support threshold and a lower minimum support threshold, pre-frequent itemsets are defined. On the basis of fast updated FP-tree algorithm(FUFP), this paper presents an improved algorithm based on pre-frequent itemsets which does not need to scan the original database untill the new transactions reach a certain amount. So it improves the efficiency of the update. Experimental evaluations show that the larger the size of the database, the more obvious the performance advantages of the algorithm.
(3) Customer demand is the driving force behind the development of enterprises. The diversity of customer demands leads to the diversity of the questionnaire data type. But traditional association rules mining algorithm can't handle a variety of types of data. Aiming at the limitation, this paper first defines the various data type and mining rules mode, and presents to statistic the support counts of items by similarity degree. Then a novel method based on fuzzy set theory is presented to deal with all kinds of data types in a unified way. At last a fuzzy association rules based Apriori is presented which is applied to the analysis of survey data of electric bicycles.
(4) Based on the above research work, this paper designs an information system for product continual quality improvement (ARMS) whose goal is to product high quality products with low cost and low consumption of resources and to improve customer satisfaction. The system integrates the process and quality data in different departments through the process quality language based on XML. Then on the basis of it, a mining algorithm which is proposed in this paper is used to find the relationship between the distributed process parameters combination and product quality problems, then the genetic algorithm is used to optimize these rules so that can help quality managers to adjust the settings of process parameters in order to facilitate continual quality improvement.
(1)为了构建条件FP-tree,FP-growth算法必须扫描数据库两次,这极大制约了它的应用。针对这一局限性,本文提出一种新颖的FP阵列技术,直接从FP阵列得到频繁项的计数,从而极大减少了遍历FP-tree的需要。本文将FP-tree数据结构与FP阵列有效地结合起来,分别提出了挖掘频繁项集和闭频繁项集的新算法。实验评测表明这两种算法在运行时间、内存消耗和可扩展性方面都具有稳定优良的性能,尤其对于稀疏数据库。
(2)Apriori算法和FP-growth算法都是以批处理方式处理所有事务,无法满足动态更新关联规则的需要。本文在FUFP算法的基础上提出了一种基于次频繁项的改进算法,在算法中引入两个支持度阈值：阈值上限和阈值下限。如果处理的新事务数没有达到一定的值(由两个支持度阈值和数据库的规模决定),该算法就不需要重新扫描原数据库,从而提高了关联规则更新的效率。实验评测表明数据库的规模越大,算法的性能优势越明显。
(3)传统关联规则挖掘算法不能同时处理多种类型的数据,无法适应多样性客户需求数据挖掘的需要。针对这一局限性,本文首先给出了各种数据类型的定义以及挖掘的规则模式的定义,提出用相似度统计项目的支持度计数,然后提出一种基于模糊集的新方法以统一的方式处理各种数据类型,最后提出一种基于Apriori的模糊关联规则挖掘算法,并将其应用到电动自行车问卷调查数据的关联分析。
(4)以上述研究工作为基础,本文开发了一个产品持续质量改善信息系统(ARMS),其目标是以低成本、低资源消耗为代价生产高质量产品,提高客户的满意度。ARMS由三个模块组成：流程数据集成模块、关联规则挖掘模块和关联规则优化模块。ARMS系统采用基于XML的流程质量语言将各有关部门的流程数据集成到中央数据仓库,在此基础上采用本文提出的新算法发现不同部门的流程参数组合与产品质量特性之间的关联规则,再运用遗传算法优化这些规则,从而帮助流程工程师调整流程参数的设置以持续提高产品的质量。图92幅,表19个,参考文献202篇。
Knowledge is the most valuable assets of a manufacturing enterprise. Data mining can extract valuable knowledge from all kinds of manufacturing data, which has promoted enormously the development of manufacturing technology and manufacturing mode. Association rules is one of the most important data mining technologies which can effectively find the relationship between data items. And the expressions of association rules are concise and easy to understand and explain. So association rules algorithm research has important theoretical significance and broad application prospect which has been a hot research field of data mining. In this paper, the key technologies of association rules and their application in product continual quality improvement have been studied deeply. The main innovation work is as follows:
(1) For generating conditional FP-tree, FP-growth algorithm need scanning database twice. Thus FP-growth algorithm can't adapt to the characteristics of data in dynamic real-time database. Aiming at the limitations, this paper presents a novel FP array technology. The counts of frequent items are obtained directly from FP array, thus the first scan is omitted. An improved frequent itemsets mining algorithm and a closed frequent itemsets mining algorithm are presented which use the FP-tree data structure in combination with the FP array technology. Experimental evaluations show that the two algorithms have stable superior performance in running time, memory consumption and scalability aspects especially for the sparse database.
(2) Apriori and FP-growth algorithm process all transactions in a batch way which can't adapt to the need to update association rules dynamically. This paper presents the concept of pre-frequent itemsets. Through an upper minimum support threshold and a lower minimum support threshold, pre-frequent itemsets are defined. On the basis of fast updated FP-tree algorithm(FUFP), this paper presents an improved algorithm based on pre-frequent itemsets which does not need to scan the original database untill the new transactions reach a certain amount. So it improves the efficiency of the update. Experimental evaluations show that the larger the size of the database, the more obvious the performance advantages of the algorithm.
(3) Customer demand is the driving force behind the development of enterprises. The diversity of customer demands leads to the diversity of the questionnaire data type. But traditional association rules mining algorithm can't handle a variety of types of data. Aiming at the limitation, this paper first defines the various data type and mining rules mode, and presents to statistic the support counts of items by similarity degree. Then a novel method based on fuzzy set theory is presented to deal with all kinds of data types in a unified way. At last a fuzzy association rules based Apriori is presented which is applied to the analysis of survey data of electric bicycles.
(4) Based on the above research work, this paper designs an information system for product continual quality improvement (ARMS) whose goal is to product high quality products with low cost and low consumption of resources and to improve customer satisfaction. The system integrates the process and quality data in different departments through the process quality language based on XML. Then on the basis of it, a mining algorithm which is proposed in this paper is used to find the relationship between the distributed process parameters combination and product quality problems, then the genetic algorithm is used to optimize these rules so that can help quality managers to adjust the settings of process parameters in order to facilitate continual quality improvement.
引文
[1]朱高峰.全球化时代的中国制造[M].北京：社会科学文献出版社,2003：5-15.
[2]林宋.信息化制造中的信息理论与集成方法研究[D].武汉：华中科技大学,2005.
[3]王先逵.制造技术的历史回顾与面临的挑战和机遇[J].机械工程学报,2002,38(8)：23-29.
[4]杨叔子,吴波,胡春华.网络化制造与企业集成[J].中国机械工程,2000,11(1-2)：45-48.
[5]李存荣.产品制造信息中的知识发现及其应用研究[D].武汉：武汉理工大学,2006.
[6]郑晓艳.频繁模式挖掘技术研究及其在供应链管理中的应用[D].天津：天津大学,2009.
[7]Eftem G, Mallach.决策支持系统与数据仓库系统[M].李昭智.译. 北京：电子工业出版社,2001：12-13.
[8]祁连,顾新建.制造企业中的知识管理[J].成组技术与生产自动化,2001,4：28-33.
[9]奚介荣,龚光荣.知识型制造企业与知识管理[J].机电一体化,2001,2：8-11.
[10]贾磊.机械制造过程中的知识管理的研究[D].上海：上海大学,2005.
[11]王忠浩.面向产品族开发设计的知识发现与应用研究[D].武汉：华中科技大学,2007.
[12]Piller B.J. Mass Customization (Third Edition)[M]. Wiesbaden:Gabler,2003: 23-56.
[13]李华伟,董小英,左美云.知识管理的理论与实践[M].北京：华艺出版社,2002：89-91.
[14]暴志刚.产品生命周期管理背景下的客户关系管理若干关键技术研究[D].杭州：浙江大学,2007.
[15]CIMData Inc.cPDm:The key to harnessing innovation in an E-business world [EB/OL]. http://www.cimdata.com/index.htm.
[16]徐河杭.面向PLM的数据挖掘技术和应用研究[D].杭州：浙江大学,2010.
[17]张立权.基于模糊推理系统的工业过程数据挖掘[D].大连：大连理工大学,2006.
[18]Fayyad U.M, Piatetsky-Shapiro G, Smyth P, et al. Advances in Knowledge Discovery and Data Mining[M]. AAAI/MIT Press,1996:167-194.
[19]Jiawei Han,Micheline Kamber.数据挖掘-概念与技术(第二版)[M].范明,孟小峰.译.北京：机械工业出版社,2007：70-93,146-168.
[20]Guh R.S. A hybrid learning based model for online detection and analysis of control chart patterns [J]. Computers & Industrial Engineering,2005,49:35-62.
[21]Liu Y.H, Huang H.P,&Lin Y.S. Attribute selection for the scheduling of flexible manufacturing systems based on fuzzy set theoretic approach and genetic algorithm[J]. Journal of the Chinese institute of industrial engineers,2005,22(1):46-55.
[22]Li D, Wu C.S, Tsai T.I, et al. Using mega-fuzzification and data trend estimation in small dataset learning for early FMS scheduling knowledge[J]. Computers &Operations Research,2006,33:1857-1869.
[23]Koonce, D.A, Tsai, S.C. Using data mining to find patterns in genetic algorithm solutions to a job shop schedule[J]. Computers & Industrial Engineering,2000,38: 361-374.
[24]Belz R, Mertens, P. Combining knowledge based systems and simulation to solve rescheduling problems [J]. Decision Support Systems,1996,17:141-157.
[25]Lee S.G, Ng Y.C. Hybrid case-based reasoning for on-line product fault diagnosis [J]. International Journal of Advanced Manufacturing Technology,2006,27:823-840.
[26]Fountain T, Dietterich T, et al. Data mining for manufacturing control:An application in optimizing IC test [M]. Exploring artificial intelligence in the new millennium, G lakemeyer and B. Nebel, eds., Morgan Kaufmann, San Francisco, CA, 2002:381-400.
[27]Maki H, Teranishi Y. Development of Automated Data Mining System for Quality Control in Manufacturing [M]. Lecture Notes in Computer Science, Berlin: Springer-Verlag,2001:93-100.
[28]Maki H, Maeda A, Morita T, et al. Applying data mining to data analysis in manufacturing [C]. International conference on advances in production management systems,2000:324-331.
[29]Shen L, Tay F.E.H, Qu L.S, et al. Fault diagnosis using rough set theory [J]. Computers in industry,2000,43:61-72.
[30]Caskey K.R. A manufacturing problem solving environment combining evaluation, search and generalization methods [J]. Computers in industry,2001, 44:175-187.
[31]Menon R, Tong L.H, Sathiyakeerthi S. Analyzing textual databases using data mining to enable fast product development process [J]. Reliability Engineering &System Safety,2005,88:171-180.
[32]Neaga E.I, Harding J.A. An enterprise modeling and integration framework based on knowledge discovery and data mining [J]. International Journal of Production Research,2005,43(6):1089-1108.
[33]Chen N, Zhu D.D, Wang W. Intelligent material processing by hyper space data mining [J]. Engineering applications of artificial intelligence,2000,13:527-532.
[34]Holden T, Serearuno M. A hybrid artificial intelligence approach for improving yield in precious stone manufacturing [J]. Journal of Intelligent Manufacturing,2005, 16:21-38.
[35]Gertosio C, Dussauchoy A. Knowledge discovery from industrial databases [J]. Journal of Intelligent Manufacturing,2004,15:29-37.
[36]Romanowski CJ, Nagi R. Analysing maintenance data using data mining methods. Data Mining for Design and Manufacture:Methods and Applications [M]. Dordrecht:Kluwer Academic Publisher,2001:161-178.
[37]Batanov D, Nagarur N, Nitikhumkasem P. Expert-MM:A knowledge based system for maintenance management [J]. Artificial Intelligence in engineering,1993, 8:283-291.
[38]Kusiak A, Kurasek C. Data mining of printed circuit board defects [J]. IEEE Transactions of Robotics and Automation,2001,17(2):191-196.
[39]Dengiz O, Smith A.E, Nettleship I. Two stage data mining for flaw identification in ceramics manufacturing [J]. International Journal of Productive Research,2006, 44(14):2839-1851.
[40]Bergeret F, Gall C.L. Yield improvement using statistical analysis of process dates [J]. IEEE Transactions on Semiconductor Manufacturing,2003,16(3):535-542.
[41]Dabbas R.M, Chen H.N. Mining semiconductor manufacturing data for productivity improvement-An integrated relational database approach [J]. Computer in Industry,2001,45:29-44.
[42]Rokach L, Maimon O. Data mining for improving the quality of manufacturing: A feature set decomposition approach [J]. Journal of Intelligent Manufacturing,2006, 17:285-299.
[43]Kwak C, Yih Y. Data-mining approach to production control in the computer-integrated testing cell [J]. IEEE Transactions on Robotics and Automation, 2004,20(1):107-116.
[44]Irani K.B, Cheng J, Fayyad U.M, et al. Applying machine learning to semiconductor manufacturing [J]. IEEE Expert,1993,8(1):41-47.
[45]Skormin V.A, Gorodetski V.I, PopYack I.J. Data mining technology for failure of prognostic of avionics [J]. IEEE Transactions on Aerospace and Electronics Systems, 2002,38(2):388-401.
[46]Jeong M.K, Lu J.C, Huo X, et al. Wavelet-based data reduction techniques for process fault detection [J]. Technometrics,2006,48(1):26-40.
[47]Rojas A, Nandi A.K. Practical scheme for fast detection and classification of rolling element bearing faults using support vector method [J]. Mechanical Systems and Signal Processing,2006,20:1523-1536.
[48]Peng Y. Intelligent condition monitoring using fuzzy inductive learning [J]. Journal of Intelligent Manufacturing,2004,15:373-380.
[49]Pasek Z.J. Exploration of rough sets theory use for manufacturing process monitoring [C]. Proceedings of the Institution of Mechanical Engineers. Journal of Engineering Manufacturing,2006, Part B,220:365-373.
[50]Hou T.S, Liu W.L, Lin L. Intelligent remote monitoring and diagnosis of manufacturing process using an integrate approach of neural networks and rough sets [J]. Journal of Intelligent Manufacturing,2003,14:239-253.
[51]Hou T.H, Huang C.C. Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction [J]. Journal of Intelligent Manufacturing,2004,15:395-408.
[52]Braha D. Shmilovici A. Data mining for improving a cleaning process in the semiconductor industry [J]. IEEE Transactions on Semiconductor Manufacturing, 2002,15(1):91-101.
[53]McDonald C.J. New tools for yield improvement in integrated circuit manufacturing:Can they be applied to reliability? [J]. Microelectronics Reliability, 1999,39(6-7):731-739.
[54]Wang C.H, Kuo W, Bensmail H. Detection and classification of defects patterns on semiconductor wafers [J]. HE Transactions,2006,38:1059-1068.
[55]Kusiak A. Data mining and decision making. In SPIE Conference on Data mining and knowledge discovery:theory, tools and technology Ⅳ [M]. Orlando, FL,2002: 155-165.
[56]Kusiak A. A data mining approach for generation of control signatures [J]. Journal of Manufacturing science and engineering,2002,124:923-926.
[57]Gardner M, Bieker J. Data mining solves tough semiconductor manufacturing problems [C]. Proc of KDD2000, Boston, MA, USA,2000:376-383.
[58]Chien C.F, Wang W.C, Cheng J.C. Data mining for yield enhancement in semiconductor manufacturing and an empirical study [J]. Expert Systems with Applications,2007,33:192-198.
[59]Sebzalli Y.M, Wang X.Z. Knowledge discovery from process operational data using PC A and fuzzy clustering [J]. Engineering Applications of Artificial Intelligence, 2001,14:607-616.
[60]Jin Y, Ishino Y. DAKA:Design activity knowledge acquisition through data mining [J]. International Journal of Production Research,2006,44(15):2813-2837.
[61]Kim P, Ding Y Optimal engineering system design guided by data-mining methods [J]. Technometrics,2005,47(3):336-348.
[62]Romanowski C.J, Nagi R. A data mining for knowledge acquisition in engineering design:A research agenda. In D.Braha (Ed.), Data mining for design and manufacture:Methods and Applications [M]. Dordrecht:Kluwer Academic Publisher, 2001:161-178.
[63]Romanowski C.J, Nagi R.A data mining approach to forming generic bills of material in support of variant design activities [J]. ASME Journal of Computing and Information Science in Engineering,2004,4(4):316-328.
[64]Kusiak A, Kernstine K.H, Kern J.A, Mclaughlin K.A., et al. Data mining: Medical and engineering case studies [C]. In Industrial Engineering Research Conference, Ohio, Cleveland,2000:1-7.
[65]Lee J.H, Yu S.J, Park S.C. Design of intelligent data sampling methodology based on data mining [J]. IEEE Transactions on Robotics and Automation,2001, 17(5):637-649.
[66]Crespo F, Weber R.A. methodology for dynamic data mining based on fuzzy clustering [J]. Fuzzy Sets and Systems,2005,150:267-284.
[67]Huang C.L, Li T.S, Peng T.K. A hybrid approach of rough set theory and genetic algorithm for fault diagnosis [J]. International Journal of Advanced Manufacturing Technology,2005,27:119-127.
[68]Hui S.C, Jha G. Data mining for customer service support [J]. Information &Management,2000,38:1-13.
[69]Symeonidis A.L, Kehagias D.D, Mitkas P.A. Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques [J]. Expert systems with applications,2003,25:589-602.
[70]Qian Z, Jiang W, Tsui K.L. Churn detection via customer profile modeling, International [J]. Journal Production Research,2006,44(14):2913-2933.
[71]Chen M.C, Huang C.L, Chen K.Y, Wu H.P. Aggregation of orders in distribution centers using data mining [J]. Expert system with application,2005,28:453-460.
[72]Tseng T.L, Kwon Y, Ho J, Jiang F. Hybrid data mining and type Ⅱ fuzzy system approach for surface finish form the perspective of E-manufacturing [C]. Proceedings of the society for photo-instrumentation engineering,2005:1-12.
[73]Feng C.X.J, Wang X.F. Data mining technique applied to predictive modeling of the knurling process [J]. ⅡE Transactions,2004,36:253-263.
[74]Kim S.H, Lee C.M. Non linear prediction of manufacturing system through explicit and implicit data mining [J]. Computers and Industrial Engineering,1997, 33(3-4):461-464.
[75]Mere J.B.O, Marcos A.G, Gonzalez J.A, Rubio V.L. Estimation of mechanical properties of steel strip in hot dip galvanizing lines [J]. Iron making and Steel making, 2004,31(1):43-50.
[76]Yuan B, Wang X.Z, Morris T. Software analyzer design using data mining technology for toxicity prediction of aqueous effluents [J]. Waste Management,2000, 20:677-686.
[77]Sylvain L, Fazel F, Stan M. Data mining to predict aircraft component replacement [J]. IEEE Intelligent Systems,1999,14(6):59-65.
[78]Lin C.C, Tseng Y.H. A neural network application for reliability modeling and condition-based predictive maintenance[J]. International Journal of Advance Manufacturing Technology,2005,25:174-179.
[79]Yam R.C.M, Tse P.W, Li L, Tu P. Intelligent predictive decision support system for condition based maintenance [J]. International Journal of advance Manufacturing Technology,2001,17:383-391.
[80]Tsai C.Y, Chiu C.C, Chen J.S. A case based reasoning system for PCB defect prediction [J]. Expert Systems with Applications,2006,28:813-822.
[81]Ozturk A, Kayaligil S, Ozdemirel N.E. Manufacturing lead time estimation using data mining [J]. European Journal of Operational Research,2006,73:683-700.
[82]Sha D.Y, Liu C.H. Using data mining for due date assignment in a dynamic job shop environment [J]. International Journal of Advance Manufacturing Technique, 2005,25:1164-1174.
[83]Song C., Guan X, Zhao Q, Ho Y.C. Machine learning approach for determining feasible plan of a remanufacturing systems [J]. IEEE Transactions on Automation Science and Engineering,2005,2(3):262-275.
[84]Wang K.J, Chen J.C, Lin Y.S. A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory [J]. Production Planning and Control,2005,16(6):665-680.
[85]Chang P.C, Hieh J.C, Liao T.W. Evolving fuzzy rules for due date assignment problem in semiconductor manufacturing factory [J]. Journal of Intelligent Manufacturing,2005,16(4-5):549-557.
[86]Morita T, Sato Y, Ayukawa E, Maeda A. Customer relationship management through data mining [C]. Informs-Korms2000, Seoul,2000:1959-1963.
[87]Tseng T. L, Huang C.C, Jiang F, Ho J.C. Applying a hybrid data mining approach to prediction problems:A case of preferred supplier prediction [J]. International Journal of Production Research,2006,44(14):2935-2954.
[88]Kusiak A. Feature transformation methods in data mining [J]. IEEE Transactions on Electronics packaging manufacturing,2001,24(3):214-221.
[89]Kusiak A. Decomposition in data mining:An industrial case study [J]. IEEE Transactions on Electronics packaging manufacturing,2000,23(4):345-353.
[90]Last M, Kandel A. Discovering useful and understandable pattern in manufacturing data [J]. Robotics and Autonomous Systems,2004,49:137-152.
[91]Backus P, Janakiram M, Movzoon S, et al. Factory cycle time prediction with a data-mining approach [J]. IEEE Transactions on Semiconductor Manufacturing,2006, 19(2):252-258.
[92]Giess M.D, Culley S.J. Investigating manufacturing data for use within design [C]. ICED 03, Stockholm, Sweden,2003:1408-1413.
[93]Giess M.D, Culley S.J, Shepherd A. Informing design using data mining methods [C]. ASME DETC, Montreal, Canada,2002:98-106.
[94]Ho G.T.S, Lau H.C.W, Lee C.K.M, Ip A.W.H, et al. An intelligent production workflow mining system for continual quality enhancement [J]. International Journal of Advanced Manufacturing Technology,2006,28:792-809.
[95]Li T.S, Huang, C.L, Wu Z.Y. Data mining using genetic programming for construction of a semiconductor manufacturing yield rate prediction system [J]. Journal of Intelligent Manufacturing,2006,17:355-361.
[96]Kang B.S, Lee J.H, Shin C.K,et al. Hybrid machine learning system for integrated yield management in semiconductor manufacturing [J]. Expert System with Application,1998,15:123-132.
[97]蔡自兴,徐光佑.人工智能及其应用(第4版)[M].北京：清华大学出版社,2010：158-169,188-192.
[98]Harding J. A, Shahbaz M, Srinivas, et al. Data Mining in manufacturing:A review [J]. Journal of Manufacturing Science and Engineering,2006,128(4): 969-976.
[99]Neaga E.I, Harding J.A. A Review of data mining techniques and software systems to improve business performance in extended manufacturing enterprises [J]. International Journal of Advanced Manufacturing System,2002,5(1):3-19.
[100]Neaga E.I, Harding J.A. Data mining techniques for supporting manufacturing enterprise design [C]. International Conference on Industrial and Production Management, Quebec City, Canada,2001:232-241.
[101]Chapman, P., Clinton, J., Kerber, R.,et al. CRISP-DM 1.0 Step-By-Step Data Mining Guide[M]. Chicago:Publish House of the Central China University of Technology and Science,1999:34-65.
[102]Agard B, Kusiak A. Data-mining based methodology for the design of product families [J]. International Journal of Production Research,2004,42(15):2955.-2969.
[103]Jiao J, Zhang Y. Product portfolio identification based on association rule mining [J]. Computer Aided Design,2005,37(2):149-172.
[104]Jiao J, Zhang Y, Helander M. A kansei mining system for affective design [J]. Expert Systems with Applications,2006,30(4):658-673.
[105]Zhang Y, Jiao J. An associative classification based recommendation system for personalization in B2C e-commerce application [J]. Expert Systems with Applications, 2007,33(2):357-367.
[106]Shao X.Y, Wang Z.H, Li P.G., et al. Integrating data mining and rough set for customer group-based discovery of product configuration rules [J]. International Journal of Production Research,2006,44(14):2789-2811.
[107]Chen M.C. Configuration of cellular manufacturing systems using association rule induction [J]. International Journal of Production Research,2003,41(2): 381-395.
[108]Chen M.C, Wu H.-P, Lin C.-P. A data mining based clustering approach to group technology [C]. In:Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan,2003:3554-3558.
[109]Agard B, Kusiak A. Data mining for subassembly selection [J]. Journal of Manufacturing Science and Engineering,2004,126:627-631.
[110]Jain V, Benyoucef L, Deshmukh S.G. A new approach for evaluating agility in supply chains using fuzzy association rules mining [J]. Engineering Applications of Artificial Intelligence,2008,21(3):367-385.
[111]Chen W.C, Tseng S.S, Wang C.Y. A novel manufacturing defect method using association rule mining techniques [J]. Expert systems with applications,2005,29: 807-815.
[112]Cunha D, Agard B, Kusiak A. Data mining for improvement of product quality [J]. International Journal of Production Research,2006,44(18-19):4027-4041.
[113]Kondo, Y. Quality and humanity [J]. TQM Magazine,1999,11(6):384-388.
[114]H.C.W.Lau, GT.S.Ho, K.F.Chu, William Ho, C.K.M.Lee. Development of an intelligent quality management system using fuzzy association rules [J]. Expert Systems with Application,1999,36:1801-1815.
[115]Stein,E.W., Miscikowski,D.K. FAILSAFE:supporting product qulity with knowledge-based systems[J]. Expert Systems with Applications,1999,16:365-377.
[116]Ament,Ch.,Goch,G. A learning fuzzy control approach to improve manufacturing quality[J]. IEEE Proc.Part D,1999,146(9):156-161.
[117]Leitch, R.R., Kraft, R., Luntz, R. RESCU:A real-time knowledge-based system for process control [J]. IEEE Proc.Part D,1991,138(3):217-227.
[118]Narayanan, S. Knowledge-based action representations for metaphor and aspect (KARMA)[D]. Berkeley:the University of California,1997.
[119]Feng, S., Li, L.X., Cen, L. An object-oriented intelligent design tool to aid the design of manufacturing systems[J]. Knowledge-based Systems,2001,14:225-232.
[120]Lee,W.B., Lau,H.C.W. Multi-agent modeling of dispersed manufacturing networks [J]. Expert Systems with Application,1999,16:297-306.
[121]Karageorgos,A., Mehandjieve,N., Weichart,G.,et al. Agent-based optimisation of logistics and production planning[J].Engineering Applications of Artificial Intelligence,2003,16(4):335-348.
[122]North,M., Macal,C, Campbell,P. Agent-based behavioral representations in problem solving enviroments. Future Generation Computer Systems,2005, 21(7):1192-1198
[123]Jia,H.Z., Ong,S.K., Fuh,J.Y.H.,et al. An adaptive and upgradeable agent-based system for coordinated product development and manufacture [J]. Robotics and Computer-Integrated Manufacturing,2004,20(2):79-90.
[124]Chen, Jian. A predictive system for blast furnaces by integrating a neural network with qualitative analysis[J].Engineering Applications of Artificial Intelligence,2001,14(1):77-85.
[125]Bozdag, Cafer Erhan., Cengiz,Kahraman., Ruan, Da. Fuzzy group decision making for selection among computer integrated manufacturing systems [J]. Computers in Industry,2003,51(1):13-29.
[126]Rezayat, Fahimeh. Constrained SPSA controller for operations processes[C]. Proceedings of the American Control Conference, Philadelhia, Pennsylvania, 1998:2698-2702.
[127]Sterjovski,Z., Nolan, D., Carpenter, D.P.Dunne.,&Norrish, J. Artificial neural networks for modeling the mechanical properties of steels in various applications [J]. Journal of Materials Processing Technology,2005,170(30):536-544.
[128]Abburi, N.R., Dixit, U.S. A knowledge-based system for the prediction of surface roughness in turning process [J]. Robotics and Computer-Integrated Manufacturing,2006,22:363-372.
[129]Li, Erguo, Jia, Li, Yu, Jinshou. A genetic neural fuzzy system-based quality prediction model for injection process [J]. Computers and Chemical Engineering, 2002,26:1253-1263.
[130]Lou, Helen H, Huang, Yinlun L. Hierarchical decision making for proactive quality control:System development for defect reduction in automotive coating operation [J]. Engineering Application of Artificial Intelligence,2003,16:237-250.
[131]Tseng T.L, Jothishanker M.C, Wu T. Quality control problem in printed circuit board manufacturing-An extended rough set theory approach [J]. Journal of Manufacturing System,2004,23(1):56-72.
[132]Tseng T.L, leeper T, Banda C, Herren S.M, et al. Quality assurance in machining process using data mining [C]. In Proceedings of Industrial Engineering Research Conference, Houston, Taxes, May 15-19,2004:1-6.
[133]Tseng T.L, Kwon Y, Ertekin Y.M. Feature-based rule induction in machining operation using rough set theory for quality assurance [JJ. Robotics and Computer Integrated Manufacturing,2005,21:559-567.
[134]Oh S, Han J, Cho H. Intelligent Process Control System for quality improvement by data mining in the process industry [M]. Data Mining for design and manufacturing:Methods and Applications, D.Braha, ed., Kluwer Academic, Dordrecht,2001:289-310.
[135]Agrawal R, Srikant R. Fast algorithm for mining association rules [C]. In the International Conference on Very Large Data Bases (VLDB'94),Sept 1994:487-499.
[136]Park J.S, Chen M.S, Yu P.S. An effective hash-based algorithm for mining association rules [C]. In:Proceedings of 1995 ACM-SIGMOD International Conference on Management of Data(SIGMOD'95), San Jose, CA, May 1995: 175-186.
[137]Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large database [C]. In:Proceedings of 1995 International Conference on Very Large Data Bases (VLDB'95), Zurich, Switzerland, Sept 1995: 432-443.
[138]Toivonen H. Sampling large databases for association rules [C], In:Proceedings of 1996 Very Large Data Bases (VLDB'96), Bombay, India, Sept 1996:134-145.
[139]Brin S, Motwani R, Ullman J.D, et al. Dynamic itemset counting and implication rules for market basket analysis [C]. In:Proceedings of 1997 ACM-SIGMOD International Conference on Management of Data (SIGMOD'97), Tucson, AZ, May 1997:255-264.
[140]Orlando S, Lucchese C, Palmerini P, et al. kDCI:a multi-strategy algorithm for mining frequent sets[C]. In Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations (FIMI'03), Melbourne, FL, Nov 2003:560-584.
[141]Han J., Pei P., Yin Y. Mining frequent patterns without candidate generation[C]. In the ACM SIGMOD International Conference on Management of Data (SIGMOD'00), May 2000:1-12.
[142]Wang K, Tang L, Han J, et al. Top down FP-growth for association rule mining [C]. In:Proceedings of the 6th Pacific Area Conference on Knowledge Discovery and Data Mining (PAKDD),2002:1430-1445.
[143]Pei J, Han J, Lu H, Nishio S, Tang S, et al. H-mine:Hyper-structure mining of frequent patterns in large databases [C]. In:Proceedings of IEEE International Conference on Data Mining,2001:441-448.
[144]Pietracaprina A, Zandlin D. Mining frequent itemsets using patricia tries [C]. In: Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations (FIMI'03'), Melbourne, FL, Nov 2003:345-357.
[145]MJ.Zaki. Scalable algorithms for association mining[J]. In IEEE Transactions on Knowledge and Data Mining,2000,12(3):372-390.
[146]M.J.Zaki, Karam Gouda. Fast vertical mining using diffsets[C]. In 9th International Conference on Knowldege Discovery and Data Mining, Washington, DC, August 2003:89-104.
[147]Pasquier N, Bastide Y, Taouil R, et al. Discovering frequent closed itemsets for association rules[C]. In ICDT'99, Jan 1999.
[148]Zaki M.J, Hsiao C. CHARM:An efficient algorithm for closed itemset mining [C]. In the second SI AM International Conference on Data Mining, Arlington, April 2002:452-467.
[149]Pei J, Han J, Mao R. CLOSET:An efficient algorithm for mining frequent closed itemsets [C]. In the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,2000:21-30.
[150]Claudio Lucchese, Salvatore Orlando, Raffaele Perago. Fast and memory efficient mining of frequent closed itemsets [J]. IEEE Transactions on knowledge and data engineering,2006,18(1):21-36.
[151]Liu G, Lu H, Yu J., et al. AFOPT:An efficient implementation of pattern growth approach[C]. In Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations(FIMI'03), Melbourne, FL, Nov 2003.
[152]Wang J, Han J, Pei J. CLOSET+:Searching for the best strategies for mining frequent closed itemsets [C]. In the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C., Aug 2003:145-159.
[153]Uno T, Asai T, Uchida Y, et al. LCM:An efficient algorithm for enumerating frequent closed item sets. In Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations(FIMI'03), Melbourne, FL, Nov 2003.
[154]Cheung D. W, Han J, Ng V. T, et al. Maintenance of discovered association rules in large databases:An incremental updating approach [C]. In:Proceedings of the 12th IEEE International Conference on Data Mining. November 1996:106-114.
[155]Cheung D.W, Lee S.D, Kao B. A general incremental technique for maintaining discovered association rules [C]. In:Proceedings of the International Conference on Database Systems for Advanced Applications,1997:350-373.
[156]Lee S.D, Cheung D.W. Maintenance of discovered association rules:when to update? Research issues on data mining and knowledge discovery,1997:870-894.
[157]冯玉才,冯剑林.关联规则的增量式更新算法[J].软件学报,1998,9(4)：301-306.
[158]杨明,孙志挥,宋余庆.快速更新全局频繁项目集[J].软件学报,2004, 15(8)：1189-1197.
[159]朱玉全,孙志挥,季小俊.基于频繁模式树的关联规则增量式更新算法[J].计算机学报,2003,26(1)：91-96.
[160]Hong T.P, Lin J.W, Wu Y.L. A fast updated frequent pattern tree [C]. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2006:2167-2172.
[161]Aggarwal C.C, Yu P.S. A new framework for itemset generation [M]. In: Proceedings of 1998 ACM Symp. Principles of Databases Systems (PODS'98), Seattle, WA, June 1999:18-24.
[162]Brin S, Motwani R, Silverstein C. Beyond market basket:Generalizing association rules to correlations [M]. In:Proceedings of 1997 ACM-SIGMOD International Conference on Management of Data (SIGMOD'97), Tucson, AZ, May 1997:265-276.
[163]Brin S, Motwani R, Ullman J.D, et al. Dynamic itemset counting and implication rules for market basket analysis [C]. In:Proceedings of 1997 ACM-SIGMOD International Conference on Management of Data (SIGMOD'97), Tucson, AZ, May 1997:255-264.
[164]Imielinski T, Khachiyan L, Abdulghani A. Cubegrades:Generalizing association rules [J]. Data Mining and Knowledge Discovery,2002,6:219-258.
[165]Lee Y.K, Kim W.Y, Cai Y.D, et al. CoMine:Efficient mining of correlated patterns [C]. In:Proceedings of 2003 International Conference on Data Mining (ICDM'03), Melbourne, FL, Nov 2003:581-584.
[166]Srikant R, Agrawal R. Mining generalized association rules [C]. In:Proceedings of the International Conference on Very Large Data Bases(VLDB'95), Sept 1995: 407-419.
[167]Jiawei Han, Yongjian Fu. Ming multiple-level association rules in large databases [J]. IEEE Transactions on knowledge and data engineering,1999,11(6): 234-246.
[168]Cai C.H, Fu A.W.C, Cheng C.H, et al. Mining association rules with weighted items [C]. In:Proceedings of the 1998 International Conference on Database Engineering and Applications Symposium, Cardiff, Wales, UK,1998:68-77.
[169]Tao F, Murtagh F, Farid M. Weighted association rule mining using weighted support and significance framework [C]. In:Proceedings of the 9th ACM SIGKDD, Washington, DC, USA,2003:661-666.
[170]Dehaspe L, de Raedt L. Mining association rules in multiple relations [C]. In: Proceedings of the 7th International Conference on Inductive Logic Programming. LNAI 1297, Berlin:Springer-Verlag,1997:125-132.
[171]Nijssen S, Kok J. Faster association rules for multiple relations [C]. In: Proceedings of the 17th International Conference on Artificial Intelligence (IJCAI 2001),2001,2:891-896.
[172]Jensen V.C, Soparker N. Frequent itemset counting across multiple tables [C]. In:Proceedings of the 4th Pacific-Asia International Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications. LNCS 1805, Berlin:Springer-Verlag,2000:49-61.
[173]Ng E.K.K, Fu A.W, Wang K. Mining association rules from stars [C]. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002).Los Alamitos:IEEE Computer Society,2002:322-329.
[174]Xu L, Xie K. A novel algorithm for frequent itemset mining in data warehouses [J]. Journal of Zhejiang University,2006,7(2):216-224.
[175]Giannella C, Han J, Pei J, et al. Mining frequent patterns in data streams at multiple time granularities [C]. In the ACM SIGMOD international conference on management, Wisconsin, June 2002:635-645.
[176]Lin C.H, Chiu D.Y, Wu Y.H, et al. Mining frequent itemsets from data streams with a time-sensitive sliding window [C]. In Proceedings of the SIAM international conference on data mining, April 2005:672-688.
[177]Helen Pinto, Jiawei Han, Jian Pei, et al. Multidimensional sequential pattern mining [C]. In:Proceedings of the international conference on information and knowledge management,2001:652-667.
[178]Chung-Ching Yu, Yen-Liang Chen. Mining sequential patterns from multidimensional sequence data [J]. IEEE Transactions on knowledge and data engineering, January 2005,17(1):43-56.
[179]Parthasarathy S, Zaki M.J, Ogihara M, et al. Incremental and interactive sequence mining [C]. In:Proceedings of the International Conference on Information and Knowledge Management,1999:732-744.
[180]Adriano Veloso, Wagner Meira Jr, Marcio Carvalho, et al. Parallel incremental and interactive mining for frequent itemsets in evolving databases [C]. In the international workshop on high performance data mining:pervasive and data stream mining, May 2003:32-40.
[181]Amol Ghoting, Srinivasan Parthasarathy. Facilitating interactive distributed data stream processing and mining [C]. In the IEEE international symposium on parallel and distributed processing system, April 2004:112-120.
[182]Matthew Eric Otey, Chao Wang, Srinivasan Parthasarathy, Adriano Veloso, Wagner Meira Jr. Mining frequent itemsets in distributed and dynamic database [C]. In Proceedings of the international conference on data mining,2003:432-444.
[183]Matthew Eric Otey, Chao Wang, Srinivasan Parthasarathy, Adriano Veloso, Wagner Meira Jr. Parallel and distributed methods for incremental frequent itemset mining [J]. IEEE Transaction on system, man and cybernetics,2004,34:67-78.
[184]Adriano Veloso, Matthew Eric Otey, Srinivasan Parthasarathy, Wagner Meira Jr. Parallel and distributed frequent itemset mining on dynamic datasets [C]. In Proceedings of the international conference on high performance computing,2003: 763-774.
[185]Assaf Schuster, Ran Wolff, Dan Trock. Distributed algorithm for mining association rules [C]. In Proceedings of the IEEE international conference on data mining, November 2003:534-551.
[186]Ran Wolff, Assaf Schuster. Association rule mining in peer-to-peer systems [J]. IEEE Transactions on systems, man, cybernetics,2004,34(6):27-42.
[187]Heike Hofmann, Arno P.J.M.Siebes, Adalbert F.X.Wilhelm. Visualizing association rules with interactive mosaic plots [C]. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, August 2000:356-372.
[188]Dario Bruzzese, Paolo Buono. Combining visual techniques for association rules exploration [C]. In the working conference on advanced visual interface, May 2004:452-466.
[189]周晓东,邹国胜,谢洁飞,张双杰.大规模定制研究综述[J].计算机集成制造系统,2003,9(12)：1045-1052.
[190]约瑟夫·派恩二世.大批量定制-企业竞争的新前沿[M].北京：中国人民大学出版社,2000：3-20.
[191]祁国宁,杨青海.大批量定制生产模式综述[J].中国机械工程,2004,15(14)：1240-1245.
[192]毋涛.面向军工企业大批量定制生产的知识管理体系研究[D].西安：西北工业大学,2007.
[193]梁春霞,谭建荣,谢清.面向MC配置设计的客户需求交互系统及其实现 研究[J].机床与液压,2005,3：11-19.
[194]延鹏,赵丽萍,王冠群,聂庆峰.面向客户需求分析的产品模块化配置方法[J].计算机集成制造系统,2010,11：34-42.
[195]朱凌云.面向大规模定制产品设计的客户需求处理关键技术研究[D].合肥：合肥工业大学,2008.
[196]齐二石,焦建新等.基于功能需求模式识别的变异式产品需求分析建模方法及其在产品设计中的应用[J].系统工程理论与实践,1999,(3)：13-23.
[197]俞立.基于产品族的产品定义中智能技术的应用研究[D].上海：上海交通大学,2009.
[198]楼健人,张树有,谭建荣.面向大批量定制的客户需求信息表达与处理技术研究[J].中国机械工程,2004,15(8)：685-687.
[199]Marshall, .G The purpose, design and administration of a questionnaire for data collection[J]. Radiography,2005,11(2):131-136.
[200]Ho G.T.S, Lau H.C.W, Lee C.K.M, et al. An intelligent forward quality enhancement system to achieve product customization [J]. Industrial Management&Data Systems,2005,105(3-4):384-406.
[201]Leung, R.W.K. Generic methodology for the design and development of an intelligent optimization system[M]. MPhil dissertation, Univ. of Polytechnic, Hong Kong,2002:34-56.
[202]Deng P.S. Tsacle E.G. Coupling genetic algorithms and rule-based systems for complex decisions[J].Expert Systems with Applications,2000,19:209-218.
[2]林宋.信息化制造中的信息理论与集成方法研究[D].武汉：华中科技大学,2005.
[3]王先逵.制造技术的历史回顾与面临的挑战和机遇[J].机械工程学报,2002,38(8)：23-29.
[4]杨叔子,吴波,胡春华.网络化制造与企业集成[J].中国机械工程,2000,11(1-2)：45-48.
[5]李存荣.产品制造信息中的知识发现及其应用研究[D].武汉：武汉理工大学,2006.
[6]郑晓艳.频繁模式挖掘技术研究及其在供应链管理中的应用[D].天津：天津大学,2009.
[7]Eftem G, Mallach.决策支持系统与数据仓库系统[M].李昭智.译. 北京：电子工业出版社,2001：12-13.
[8]祁连,顾新建.制造企业中的知识管理[J].成组技术与生产自动化,2001,4：28-33.
[9]奚介荣,龚光荣.知识型制造企业与知识管理[J].机电一体化,2001,2：8-11.
[10]贾磊.机械制造过程中的知识管理的研究[D].上海：上海大学,2005.
[11]王忠浩.面向产品族开发设计的知识发现与应用研究[D].武汉：华中科技大学,2007.
[12]Piller B.J. Mass Customization (Third Edition)[M]. Wiesbaden:Gabler,2003: 23-56.
[13]李华伟,董小英,左美云.知识管理的理论与实践[M].北京：华艺出版社,2002：89-91.
[14]暴志刚.产品生命周期管理背景下的客户关系管理若干关键技术研究[D].杭州：浙江大学,2007.
[15]CIMData Inc.cPDm:The key to harnessing innovation in an E-business world [EB/OL]. http://www.cimdata.com/index.htm.
[16]徐河杭.面向PLM的数据挖掘技术和应用研究[D].杭州：浙江大学,2010.
[17]张立权.基于模糊推理系统的工业过程数据挖掘[D].大连：大连理工大学,2006.
[18]Fayyad U.M, Piatetsky-Shapiro G, Smyth P, et al. Advances in Knowledge Discovery and Data Mining[M]. AAAI/MIT Press,1996:167-194.
[19]Jiawei Han,Micheline Kamber.数据挖掘-概念与技术(第二版)[M].范明,孟小峰.译.北京：机械工业出版社,2007：70-93,146-168.
[20]Guh R.S. A hybrid learning based model for online detection and analysis of control chart patterns [J]. Computers & Industrial Engineering,2005,49:35-62.
[21]Liu Y.H, Huang H.P,&Lin Y.S. Attribute selection for the scheduling of flexible manufacturing systems based on fuzzy set theoretic approach and genetic algorithm[J]. Journal of the Chinese institute of industrial engineers,2005,22(1):46-55.
[22]Li D, Wu C.S, Tsai T.I, et al. Using mega-fuzzification and data trend estimation in small dataset learning for early FMS scheduling knowledge[J]. Computers &Operations Research,2006,33:1857-1869.
[23]Koonce, D.A, Tsai, S.C. Using data mining to find patterns in genetic algorithm solutions to a job shop schedule[J]. Computers & Industrial Engineering,2000,38: 361-374.
[24]Belz R, Mertens, P. Combining knowledge based systems and simulation to solve rescheduling problems [J]. Decision Support Systems,1996,17:141-157.
[25]Lee S.G, Ng Y.C. Hybrid case-based reasoning for on-line product fault diagnosis [J]. International Journal of Advanced Manufacturing Technology,2006,27:823-840.
[26]Fountain T, Dietterich T, et al. Data mining for manufacturing control:An application in optimizing IC test [M]. Exploring artificial intelligence in the new millennium, G lakemeyer and B. Nebel, eds., Morgan Kaufmann, San Francisco, CA, 2002:381-400.
[27]Maki H, Teranishi Y. Development of Automated Data Mining System for Quality Control in Manufacturing [M]. Lecture Notes in Computer Science, Berlin: Springer-Verlag,2001:93-100.
[28]Maki H, Maeda A, Morita T, et al. Applying data mining to data analysis in manufacturing [C]. International conference on advances in production management systems,2000:324-331.
[29]Shen L, Tay F.E.H, Qu L.S, et al. Fault diagnosis using rough set theory [J]. Computers in industry,2000,43:61-72.
[30]Caskey K.R. A manufacturing problem solving environment combining evaluation, search and generalization methods [J]. Computers in industry,2001, 44:175-187.
[31]Menon R, Tong L.H, Sathiyakeerthi S. Analyzing textual databases using data mining to enable fast product development process [J]. Reliability Engineering &System Safety,2005,88:171-180.
[32]Neaga E.I, Harding J.A. An enterprise modeling and integration framework based on knowledge discovery and data mining [J]. International Journal of Production Research,2005,43(6):1089-1108.
[33]Chen N, Zhu D.D, Wang W. Intelligent material processing by hyper space data mining [J]. Engineering applications of artificial intelligence,2000,13:527-532.
[34]Holden T, Serearuno M. A hybrid artificial intelligence approach for improving yield in precious stone manufacturing [J]. Journal of Intelligent Manufacturing,2005, 16:21-38.
[35]Gertosio C, Dussauchoy A. Knowledge discovery from industrial databases [J]. Journal of Intelligent Manufacturing,2004,15:29-37.
[36]Romanowski CJ, Nagi R. Analysing maintenance data using data mining methods. Data Mining for Design and Manufacture:Methods and Applications [M]. Dordrecht:Kluwer Academic Publisher,2001:161-178.
[37]Batanov D, Nagarur N, Nitikhumkasem P. Expert-MM:A knowledge based system for maintenance management [J]. Artificial Intelligence in engineering,1993, 8:283-291.
[38]Kusiak A, Kurasek C. Data mining of printed circuit board defects [J]. IEEE Transactions of Robotics and Automation,2001,17(2):191-196.
[39]Dengiz O, Smith A.E, Nettleship I. Two stage data mining for flaw identification in ceramics manufacturing [J]. International Journal of Productive Research,2006, 44(14):2839-1851.
[40]Bergeret F, Gall C.L. Yield improvement using statistical analysis of process dates [J]. IEEE Transactions on Semiconductor Manufacturing,2003,16(3):535-542.
[41]Dabbas R.M, Chen H.N. Mining semiconductor manufacturing data for productivity improvement-An integrated relational database approach [J]. Computer in Industry,2001,45:29-44.
[42]Rokach L, Maimon O. Data mining for improving the quality of manufacturing: A feature set decomposition approach [J]. Journal of Intelligent Manufacturing,2006, 17:285-299.
[43]Kwak C, Yih Y. Data-mining approach to production control in the computer-integrated testing cell [J]. IEEE Transactions on Robotics and Automation, 2004,20(1):107-116.
[44]Irani K.B, Cheng J, Fayyad U.M, et al. Applying machine learning to semiconductor manufacturing [J]. IEEE Expert,1993,8(1):41-47.
[45]Skormin V.A, Gorodetski V.I, PopYack I.J. Data mining technology for failure of prognostic of avionics [J]. IEEE Transactions on Aerospace and Electronics Systems, 2002,38(2):388-401.
[46]Jeong M.K, Lu J.C, Huo X, et al. Wavelet-based data reduction techniques for process fault detection [J]. Technometrics,2006,48(1):26-40.
[47]Rojas A, Nandi A.K. Practical scheme for fast detection and classification of rolling element bearing faults using support vector method [J]. Mechanical Systems and Signal Processing,2006,20:1523-1536.
[48]Peng Y. Intelligent condition monitoring using fuzzy inductive learning [J]. Journal of Intelligent Manufacturing,2004,15:373-380.
[49]Pasek Z.J. Exploration of rough sets theory use for manufacturing process monitoring [C]. Proceedings of the Institution of Mechanical Engineers. Journal of Engineering Manufacturing,2006, Part B,220:365-373.
[50]Hou T.S, Liu W.L, Lin L. Intelligent remote monitoring and diagnosis of manufacturing process using an integrate approach of neural networks and rough sets [J]. Journal of Intelligent Manufacturing,2003,14:239-253.
[51]Hou T.H, Huang C.C. Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction [J]. Journal of Intelligent Manufacturing,2004,15:395-408.
[52]Braha D. Shmilovici A. Data mining for improving a cleaning process in the semiconductor industry [J]. IEEE Transactions on Semiconductor Manufacturing, 2002,15(1):91-101.
[53]McDonald C.J. New tools for yield improvement in integrated circuit manufacturing:Can they be applied to reliability? [J]. Microelectronics Reliability, 1999,39(6-7):731-739.
[54]Wang C.H, Kuo W, Bensmail H. Detection and classification of defects patterns on semiconductor wafers [J]. HE Transactions,2006,38:1059-1068.
[55]Kusiak A. Data mining and decision making. In SPIE Conference on Data mining and knowledge discovery:theory, tools and technology Ⅳ [M]. Orlando, FL,2002: 155-165.
[56]Kusiak A. A data mining approach for generation of control signatures [J]. Journal of Manufacturing science and engineering,2002,124:923-926.
[57]Gardner M, Bieker J. Data mining solves tough semiconductor manufacturing problems [C]. Proc of KDD2000, Boston, MA, USA,2000:376-383.
[58]Chien C.F, Wang W.C, Cheng J.C. Data mining for yield enhancement in semiconductor manufacturing and an empirical study [J]. Expert Systems with Applications,2007,33:192-198.
[59]Sebzalli Y.M, Wang X.Z. Knowledge discovery from process operational data using PC A and fuzzy clustering [J]. Engineering Applications of Artificial Intelligence, 2001,14:607-616.
[60]Jin Y, Ishino Y. DAKA:Design activity knowledge acquisition through data mining [J]. International Journal of Production Research,2006,44(15):2813-2837.
[61]Kim P, Ding Y Optimal engineering system design guided by data-mining methods [J]. Technometrics,2005,47(3):336-348.
[62]Romanowski C.J, Nagi R. A data mining for knowledge acquisition in engineering design:A research agenda. In D.Braha (Ed.), Data mining for design and manufacture:Methods and Applications [M]. Dordrecht:Kluwer Academic Publisher, 2001:161-178.
[63]Romanowski C.J, Nagi R.A data mining approach to forming generic bills of material in support of variant design activities [J]. ASME Journal of Computing and Information Science in Engineering,2004,4(4):316-328.
[64]Kusiak A, Kernstine K.H, Kern J.A, Mclaughlin K.A., et al. Data mining: Medical and engineering case studies [C]. In Industrial Engineering Research Conference, Ohio, Cleveland,2000:1-7.
[65]Lee J.H, Yu S.J, Park S.C. Design of intelligent data sampling methodology based on data mining [J]. IEEE Transactions on Robotics and Automation,2001, 17(5):637-649.
[66]Crespo F, Weber R.A. methodology for dynamic data mining based on fuzzy clustering [J]. Fuzzy Sets and Systems,2005,150:267-284.
[67]Huang C.L, Li T.S, Peng T.K. A hybrid approach of rough set theory and genetic algorithm for fault diagnosis [J]. International Journal of Advanced Manufacturing Technology,2005,27:119-127.
[68]Hui S.C, Jha G. Data mining for customer service support [J]. Information &Management,2000,38:1-13.
[69]Symeonidis A.L, Kehagias D.D, Mitkas P.A. Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques [J]. Expert systems with applications,2003,25:589-602.
[70]Qian Z, Jiang W, Tsui K.L. Churn detection via customer profile modeling, International [J]. Journal Production Research,2006,44(14):2913-2933.
[71]Chen M.C, Huang C.L, Chen K.Y, Wu H.P. Aggregation of orders in distribution centers using data mining [J]. Expert system with application,2005,28:453-460.
[72]Tseng T.L, Kwon Y, Ho J, Jiang F. Hybrid data mining and type Ⅱ fuzzy system approach for surface finish form the perspective of E-manufacturing [C]. Proceedings of the society for photo-instrumentation engineering,2005:1-12.
[73]Feng C.X.J, Wang X.F. Data mining technique applied to predictive modeling of the knurling process [J]. ⅡE Transactions,2004,36:253-263.
[74]Kim S.H, Lee C.M. Non linear prediction of manufacturing system through explicit and implicit data mining [J]. Computers and Industrial Engineering,1997, 33(3-4):461-464.
[75]Mere J.B.O, Marcos A.G, Gonzalez J.A, Rubio V.L. Estimation of mechanical properties of steel strip in hot dip galvanizing lines [J]. Iron making and Steel making, 2004,31(1):43-50.
[76]Yuan B, Wang X.Z, Morris T. Software analyzer design using data mining technology for toxicity prediction of aqueous effluents [J]. Waste Management,2000, 20:677-686.
[77]Sylvain L, Fazel F, Stan M. Data mining to predict aircraft component replacement [J]. IEEE Intelligent Systems,1999,14(6):59-65.
[78]Lin C.C, Tseng Y.H. A neural network application for reliability modeling and condition-based predictive maintenance[J]. International Journal of Advance Manufacturing Technology,2005,25:174-179.
[79]Yam R.C.M, Tse P.W, Li L, Tu P. Intelligent predictive decision support system for condition based maintenance [J]. International Journal of advance Manufacturing Technology,2001,17:383-391.
[80]Tsai C.Y, Chiu C.C, Chen J.S. A case based reasoning system for PCB defect prediction [J]. Expert Systems with Applications,2006,28:813-822.
[81]Ozturk A, Kayaligil S, Ozdemirel N.E. Manufacturing lead time estimation using data mining [J]. European Journal of Operational Research,2006,73:683-700.
[82]Sha D.Y, Liu C.H. Using data mining for due date assignment in a dynamic job shop environment [J]. International Journal of Advance Manufacturing Technique, 2005,25:1164-1174.
[83]Song C., Guan X, Zhao Q, Ho Y.C. Machine learning approach for determining feasible plan of a remanufacturing systems [J]. IEEE Transactions on Automation Science and Engineering,2005,2(3):262-275.
[84]Wang K.J, Chen J.C, Lin Y.S. A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory [J]. Production Planning and Control,2005,16(6):665-680.
[85]Chang P.C, Hieh J.C, Liao T.W. Evolving fuzzy rules for due date assignment problem in semiconductor manufacturing factory [J]. Journal of Intelligent Manufacturing,2005,16(4-5):549-557.
[86]Morita T, Sato Y, Ayukawa E, Maeda A. Customer relationship management through data mining [C]. Informs-Korms2000, Seoul,2000:1959-1963.
[87]Tseng T. L, Huang C.C, Jiang F, Ho J.C. Applying a hybrid data mining approach to prediction problems:A case of preferred supplier prediction [J]. International Journal of Production Research,2006,44(14):2935-2954.
[88]Kusiak A. Feature transformation methods in data mining [J]. IEEE Transactions on Electronics packaging manufacturing,2001,24(3):214-221.
[89]Kusiak A. Decomposition in data mining:An industrial case study [J]. IEEE Transactions on Electronics packaging manufacturing,2000,23(4):345-353.
[90]Last M, Kandel A. Discovering useful and understandable pattern in manufacturing data [J]. Robotics and Autonomous Systems,2004,49:137-152.
[91]Backus P, Janakiram M, Movzoon S, et al. Factory cycle time prediction with a data-mining approach [J]. IEEE Transactions on Semiconductor Manufacturing,2006, 19(2):252-258.
[92]Giess M.D, Culley S.J. Investigating manufacturing data for use within design [C]. ICED 03, Stockholm, Sweden,2003:1408-1413.
[93]Giess M.D, Culley S.J, Shepherd A. Informing design using data mining methods [C]. ASME DETC, Montreal, Canada,2002:98-106.
[94]Ho G.T.S, Lau H.C.W, Lee C.K.M, Ip A.W.H, et al. An intelligent production workflow mining system for continual quality enhancement [J]. International Journal of Advanced Manufacturing Technology,2006,28:792-809.
[95]Li T.S, Huang, C.L, Wu Z.Y. Data mining using genetic programming for construction of a semiconductor manufacturing yield rate prediction system [J]. Journal of Intelligent Manufacturing,2006,17:355-361.
[96]Kang B.S, Lee J.H, Shin C.K,et al. Hybrid machine learning system for integrated yield management in semiconductor manufacturing [J]. Expert System with Application,1998,15:123-132.
[97]蔡自兴,徐光佑.人工智能及其应用(第4版)[M].北京：清华大学出版社,2010：158-169,188-192.
[98]Harding J. A, Shahbaz M, Srinivas, et al. Data Mining in manufacturing:A review [J]. Journal of Manufacturing Science and Engineering,2006,128(4): 969-976.
[99]Neaga E.I, Harding J.A. A Review of data mining techniques and software systems to improve business performance in extended manufacturing enterprises [J]. International Journal of Advanced Manufacturing System,2002,5(1):3-19.
[100]Neaga E.I, Harding J.A. Data mining techniques for supporting manufacturing enterprise design [C]. International Conference on Industrial and Production Management, Quebec City, Canada,2001:232-241.
[101]Chapman, P., Clinton, J., Kerber, R.,et al. CRISP-DM 1.0 Step-By-Step Data Mining Guide[M]. Chicago:Publish House of the Central China University of Technology and Science,1999:34-65.
[102]Agard B, Kusiak A. Data-mining based methodology for the design of product families [J]. International Journal of Production Research,2004,42(15):2955.-2969.
[103]Jiao J, Zhang Y. Product portfolio identification based on association rule mining [J]. Computer Aided Design,2005,37(2):149-172.
[104]Jiao J, Zhang Y, Helander M. A kansei mining system for affective design [J]. Expert Systems with Applications,2006,30(4):658-673.
[105]Zhang Y, Jiao J. An associative classification based recommendation system for personalization in B2C e-commerce application [J]. Expert Systems with Applications, 2007,33(2):357-367.
[106]Shao X.Y, Wang Z.H, Li P.G., et al. Integrating data mining and rough set for customer group-based discovery of product configuration rules [J]. International Journal of Production Research,2006,44(14):2789-2811.
[107]Chen M.C. Configuration of cellular manufacturing systems using association rule induction [J]. International Journal of Production Research,2003,41(2): 381-395.
[108]Chen M.C, Wu H.-P, Lin C.-P. A data mining based clustering approach to group technology [C]. In:Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan,2003:3554-3558.
[109]Agard B, Kusiak A. Data mining for subassembly selection [J]. Journal of Manufacturing Science and Engineering,2004,126:627-631.
[110]Jain V, Benyoucef L, Deshmukh S.G. A new approach for evaluating agility in supply chains using fuzzy association rules mining [J]. Engineering Applications of Artificial Intelligence,2008,21(3):367-385.
[111]Chen W.C, Tseng S.S, Wang C.Y. A novel manufacturing defect method using association rule mining techniques [J]. Expert systems with applications,2005,29: 807-815.
[112]Cunha D, Agard B, Kusiak A. Data mining for improvement of product quality [J]. International Journal of Production Research,2006,44(18-19):4027-4041.
[113]Kondo, Y. Quality and humanity [J]. TQM Magazine,1999,11(6):384-388.
[114]H.C.W.Lau, GT.S.Ho, K.F.Chu, William Ho, C.K.M.Lee. Development of an intelligent quality management system using fuzzy association rules [J]. Expert Systems with Application,1999,36:1801-1815.
[115]Stein,E.W., Miscikowski,D.K. FAILSAFE:supporting product qulity with knowledge-based systems[J]. Expert Systems with Applications,1999,16:365-377.
[116]Ament,Ch.,Goch,G. A learning fuzzy control approach to improve manufacturing quality[J]. IEEE Proc.Part D,1999,146(9):156-161.
[117]Leitch, R.R., Kraft, R., Luntz, R. RESCU:A real-time knowledge-based system for process control [J]. IEEE Proc.Part D,1991,138(3):217-227.
[118]Narayanan, S. Knowledge-based action representations for metaphor and aspect (KARMA)[D]. Berkeley:the University of California,1997.
[119]Feng, S., Li, L.X., Cen, L. An object-oriented intelligent design tool to aid the design of manufacturing systems[J]. Knowledge-based Systems,2001,14:225-232.
[120]Lee,W.B., Lau,H.C.W. Multi-agent modeling of dispersed manufacturing networks [J]. Expert Systems with Application,1999,16:297-306.
[121]Karageorgos,A., Mehandjieve,N., Weichart,G.,et al. Agent-based optimisation of logistics and production planning[J].Engineering Applications of Artificial Intelligence,2003,16(4):335-348.
[122]North,M., Macal,C, Campbell,P. Agent-based behavioral representations in problem solving enviroments. Future Generation Computer Systems,2005, 21(7):1192-1198
[123]Jia,H.Z., Ong,S.K., Fuh,J.Y.H.,et al. An adaptive and upgradeable agent-based system for coordinated product development and manufacture [J]. Robotics and Computer-Integrated Manufacturing,2004,20(2):79-90.
[124]Chen, Jian. A predictive system for blast furnaces by integrating a neural network with qualitative analysis[J].Engineering Applications of Artificial Intelligence,2001,14(1):77-85.
[125]Bozdag, Cafer Erhan., Cengiz,Kahraman., Ruan, Da. Fuzzy group decision making for selection among computer integrated manufacturing systems [J]. Computers in Industry,2003,51(1):13-29.
[126]Rezayat, Fahimeh. Constrained SPSA controller for operations processes[C]. Proceedings of the American Control Conference, Philadelhia, Pennsylvania, 1998:2698-2702.
[127]Sterjovski,Z., Nolan, D., Carpenter, D.P.Dunne.,&Norrish, J. Artificial neural networks for modeling the mechanical properties of steels in various applications [J]. Journal of Materials Processing Technology,2005,170(30):536-544.
[128]Abburi, N.R., Dixit, U.S. A knowledge-based system for the prediction of surface roughness in turning process [J]. Robotics and Computer-Integrated Manufacturing,2006,22:363-372.
[129]Li, Erguo, Jia, Li, Yu, Jinshou. A genetic neural fuzzy system-based quality prediction model for injection process [J]. Computers and Chemical Engineering, 2002,26:1253-1263.
[130]Lou, Helen H, Huang, Yinlun L. Hierarchical decision making for proactive quality control:System development for defect reduction in automotive coating operation [J]. Engineering Application of Artificial Intelligence,2003,16:237-250.
[131]Tseng T.L, Jothishanker M.C, Wu T. Quality control problem in printed circuit board manufacturing-An extended rough set theory approach [J]. Journal of Manufacturing System,2004,23(1):56-72.
[132]Tseng T.L, leeper T, Banda C, Herren S.M, et al. Quality assurance in machining process using data mining [C]. In Proceedings of Industrial Engineering Research Conference, Houston, Taxes, May 15-19,2004:1-6.
[133]Tseng T.L, Kwon Y, Ertekin Y.M. Feature-based rule induction in machining operation using rough set theory for quality assurance [JJ. Robotics and Computer Integrated Manufacturing,2005,21:559-567.
[134]Oh S, Han J, Cho H. Intelligent Process Control System for quality improvement by data mining in the process industry [M]. Data Mining for design and manufacturing:Methods and Applications, D.Braha, ed., Kluwer Academic, Dordrecht,2001:289-310.
[135]Agrawal R, Srikant R. Fast algorithm for mining association rules [C]. In the International Conference on Very Large Data Bases (VLDB'94),Sept 1994:487-499.
[136]Park J.S, Chen M.S, Yu P.S. An effective hash-based algorithm for mining association rules [C]. In:Proceedings of 1995 ACM-SIGMOD International Conference on Management of Data(SIGMOD'95), San Jose, CA, May 1995: 175-186.
[137]Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large database [C]. In:Proceedings of 1995 International Conference on Very Large Data Bases (VLDB'95), Zurich, Switzerland, Sept 1995: 432-443.
[138]Toivonen H. Sampling large databases for association rules [C], In:Proceedings of 1996 Very Large Data Bases (VLDB'96), Bombay, India, Sept 1996:134-145.
[139]Brin S, Motwani R, Ullman J.D, et al. Dynamic itemset counting and implication rules for market basket analysis [C]. In:Proceedings of 1997 ACM-SIGMOD International Conference on Management of Data (SIGMOD'97), Tucson, AZ, May 1997:255-264.
[140]Orlando S, Lucchese C, Palmerini P, et al. kDCI:a multi-strategy algorithm for mining frequent sets[C]. In Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations (FIMI'03), Melbourne, FL, Nov 2003:560-584.
[141]Han J., Pei P., Yin Y. Mining frequent patterns without candidate generation[C]. In the ACM SIGMOD International Conference on Management of Data (SIGMOD'00), May 2000:1-12.
[142]Wang K, Tang L, Han J, et al. Top down FP-growth for association rule mining [C]. In:Proceedings of the 6th Pacific Area Conference on Knowledge Discovery and Data Mining (PAKDD),2002:1430-1445.
[143]Pei J, Han J, Lu H, Nishio S, Tang S, et al. H-mine:Hyper-structure mining of frequent patterns in large databases [C]. In:Proceedings of IEEE International Conference on Data Mining,2001:441-448.
[144]Pietracaprina A, Zandlin D. Mining frequent itemsets using patricia tries [C]. In: Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations (FIMI'03'), Melbourne, FL, Nov 2003:345-357.
[145]MJ.Zaki. Scalable algorithms for association mining[J]. In IEEE Transactions on Knowledge and Data Mining,2000,12(3):372-390.
[146]M.J.Zaki, Karam Gouda. Fast vertical mining using diffsets[C]. In 9th International Conference on Knowldege Discovery and Data Mining, Washington, DC, August 2003:89-104.
[147]Pasquier N, Bastide Y, Taouil R, et al. Discovering frequent closed itemsets for association rules[C]. In ICDT'99, Jan 1999.
[148]Zaki M.J, Hsiao C. CHARM:An efficient algorithm for closed itemset mining [C]. In the second SI AM International Conference on Data Mining, Arlington, April 2002:452-467.
[149]Pei J, Han J, Mao R. CLOSET:An efficient algorithm for mining frequent closed itemsets [C]. In the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,2000:21-30.
[150]Claudio Lucchese, Salvatore Orlando, Raffaele Perago. Fast and memory efficient mining of frequent closed itemsets [J]. IEEE Transactions on knowledge and data engineering,2006,18(1):21-36.
[151]Liu G, Lu H, Yu J., et al. AFOPT:An efficient implementation of pattern growth approach[C]. In Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations(FIMI'03), Melbourne, FL, Nov 2003.
[152]Wang J, Han J, Pei J. CLOSET+:Searching for the best strategies for mining frequent closed itemsets [C]. In the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C., Aug 2003:145-159.
[153]Uno T, Asai T, Uchida Y, et al. LCM:An efficient algorithm for enumerating frequent closed item sets. In Proceedings of the 1st Workshop on Frequent Itemset Mining Implementations(FIMI'03), Melbourne, FL, Nov 2003.
[154]Cheung D. W, Han J, Ng V. T, et al. Maintenance of discovered association rules in large databases:An incremental updating approach [C]. In:Proceedings of the 12th IEEE International Conference on Data Mining. November 1996:106-114.
[155]Cheung D.W, Lee S.D, Kao B. A general incremental technique for maintaining discovered association rules [C]. In:Proceedings of the International Conference on Database Systems for Advanced Applications,1997:350-373.
[156]Lee S.D, Cheung D.W. Maintenance of discovered association rules:when to update? Research issues on data mining and knowledge discovery,1997:870-894.
[157]冯玉才,冯剑林.关联规则的增量式更新算法[J].软件学报,1998,9(4)：301-306.
[158]杨明,孙志挥,宋余庆.快速更新全局频繁项目集[J].软件学报,2004, 15(8)：1189-1197.
[159]朱玉全,孙志挥,季小俊.基于频繁模式树的关联规则增量式更新算法[J].计算机学报,2003,26(1)：91-96.
[160]Hong T.P, Lin J.W, Wu Y.L. A fast updated frequent pattern tree [C]. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2006:2167-2172.
[161]Aggarwal C.C, Yu P.S. A new framework for itemset generation [M]. In: Proceedings of 1998 ACM Symp. Principles of Databases Systems (PODS'98), Seattle, WA, June 1999:18-24.
[162]Brin S, Motwani R, Silverstein C. Beyond market basket:Generalizing association rules to correlations [M]. In:Proceedings of 1997 ACM-SIGMOD International Conference on Management of Data (SIGMOD'97), Tucson, AZ, May 1997:265-276.
[163]Brin S, Motwani R, Ullman J.D, et al. Dynamic itemset counting and implication rules for market basket analysis [C]. In:Proceedings of 1997 ACM-SIGMOD International Conference on Management of Data (SIGMOD'97), Tucson, AZ, May 1997:255-264.
[164]Imielinski T, Khachiyan L, Abdulghani A. Cubegrades:Generalizing association rules [J]. Data Mining and Knowledge Discovery,2002,6:219-258.
[165]Lee Y.K, Kim W.Y, Cai Y.D, et al. CoMine:Efficient mining of correlated patterns [C]. In:Proceedings of 2003 International Conference on Data Mining (ICDM'03), Melbourne, FL, Nov 2003:581-584.
[166]Srikant R, Agrawal R. Mining generalized association rules [C]. In:Proceedings of the International Conference on Very Large Data Bases(VLDB'95), Sept 1995: 407-419.
[167]Jiawei Han, Yongjian Fu. Ming multiple-level association rules in large databases [J]. IEEE Transactions on knowledge and data engineering,1999,11(6): 234-246.
[168]Cai C.H, Fu A.W.C, Cheng C.H, et al. Mining association rules with weighted items [C]. In:Proceedings of the 1998 International Conference on Database Engineering and Applications Symposium, Cardiff, Wales, UK,1998:68-77.
[169]Tao F, Murtagh F, Farid M. Weighted association rule mining using weighted support and significance framework [C]. In:Proceedings of the 9th ACM SIGKDD, Washington, DC, USA,2003:661-666.
[170]Dehaspe L, de Raedt L. Mining association rules in multiple relations [C]. In: Proceedings of the 7th International Conference on Inductive Logic Programming. LNAI 1297, Berlin:Springer-Verlag,1997:125-132.
[171]Nijssen S, Kok J. Faster association rules for multiple relations [C]. In: Proceedings of the 17th International Conference on Artificial Intelligence (IJCAI 2001),2001,2:891-896.
[172]Jensen V.C, Soparker N. Frequent itemset counting across multiple tables [C]. In:Proceedings of the 4th Pacific-Asia International Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications. LNCS 1805, Berlin:Springer-Verlag,2000:49-61.
[173]Ng E.K.K, Fu A.W, Wang K. Mining association rules from stars [C]. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002).Los Alamitos:IEEE Computer Society,2002:322-329.
[174]Xu L, Xie K. A novel algorithm for frequent itemset mining in data warehouses [J]. Journal of Zhejiang University,2006,7(2):216-224.
[175]Giannella C, Han J, Pei J, et al. Mining frequent patterns in data streams at multiple time granularities [C]. In the ACM SIGMOD international conference on management, Wisconsin, June 2002:635-645.
[176]Lin C.H, Chiu D.Y, Wu Y.H, et al. Mining frequent itemsets from data streams with a time-sensitive sliding window [C]. In Proceedings of the SIAM international conference on data mining, April 2005:672-688.
[177]Helen Pinto, Jiawei Han, Jian Pei, et al. Multidimensional sequential pattern mining [C]. In:Proceedings of the international conference on information and knowledge management,2001:652-667.
[178]Chung-Ching Yu, Yen-Liang Chen. Mining sequential patterns from multidimensional sequence data [J]. IEEE Transactions on knowledge and data engineering, January 2005,17(1):43-56.
[179]Parthasarathy S, Zaki M.J, Ogihara M, et al. Incremental and interactive sequence mining [C]. In:Proceedings of the International Conference on Information and Knowledge Management,1999:732-744.
[180]Adriano Veloso, Wagner Meira Jr, Marcio Carvalho, et al. Parallel incremental and interactive mining for frequent itemsets in evolving databases [C]. In the international workshop on high performance data mining:pervasive and data stream mining, May 2003:32-40.
[181]Amol Ghoting, Srinivasan Parthasarathy. Facilitating interactive distributed data stream processing and mining [C]. In the IEEE international symposium on parallel and distributed processing system, April 2004:112-120.
[182]Matthew Eric Otey, Chao Wang, Srinivasan Parthasarathy, Adriano Veloso, Wagner Meira Jr. Mining frequent itemsets in distributed and dynamic database [C]. In Proceedings of the international conference on data mining,2003:432-444.
[183]Matthew Eric Otey, Chao Wang, Srinivasan Parthasarathy, Adriano Veloso, Wagner Meira Jr. Parallel and distributed methods for incremental frequent itemset mining [J]. IEEE Transaction on system, man and cybernetics,2004,34:67-78.
[184]Adriano Veloso, Matthew Eric Otey, Srinivasan Parthasarathy, Wagner Meira Jr. Parallel and distributed frequent itemset mining on dynamic datasets [C]. In Proceedings of the international conference on high performance computing,2003: 763-774.
[185]Assaf Schuster, Ran Wolff, Dan Trock. Distributed algorithm for mining association rules [C]. In Proceedings of the IEEE international conference on data mining, November 2003:534-551.
[186]Ran Wolff, Assaf Schuster. Association rule mining in peer-to-peer systems [J]. IEEE Transactions on systems, man, cybernetics,2004,34(6):27-42.
[187]Heike Hofmann, Arno P.J.M.Siebes, Adalbert F.X.Wilhelm. Visualizing association rules with interactive mosaic plots [C]. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, August 2000:356-372.
[188]Dario Bruzzese, Paolo Buono. Combining visual techniques for association rules exploration [C]. In the working conference on advanced visual interface, May 2004:452-466.
[189]周晓东,邹国胜,谢洁飞,张双杰.大规模定制研究综述[J].计算机集成制造系统,2003,9(12)：1045-1052.
[190]约瑟夫·派恩二世.大批量定制-企业竞争的新前沿[M].北京：中国人民大学出版社,2000：3-20.
[191]祁国宁,杨青海.大批量定制生产模式综述[J].中国机械工程,2004,15(14)：1240-1245.
[192]毋涛.面向军工企业大批量定制生产的知识管理体系研究[D].西安：西北工业大学,2007.
[193]梁春霞,谭建荣,谢清.面向MC配置设计的客户需求交互系统及其实现 研究[J].机床与液压,2005,3：11-19.
[194]延鹏,赵丽萍,王冠群,聂庆峰.面向客户需求分析的产品模块化配置方法[J].计算机集成制造系统,2010,11：34-42.
[195]朱凌云.面向大规模定制产品设计的客户需求处理关键技术研究[D].合肥：合肥工业大学,2008.
[196]齐二石,焦建新等.基于功能需求模式识别的变异式产品需求分析建模方法及其在产品设计中的应用[J].系统工程理论与实践,1999,(3)：13-23.
[197]俞立.基于产品族的产品定义中智能技术的应用研究[D].上海：上海交通大学,2009.
[198]楼健人,张树有,谭建荣.面向大批量定制的客户需求信息表达与处理技术研究[J].中国机械工程,2004,15(8)：685-687.
[199]Marshall, .G The purpose, design and administration of a questionnaire for data collection[J]. Radiography,2005,11(2):131-136.
[200]Ho G.T.S, Lau H.C.W, Lee C.K.M, et al. An intelligent forward quality enhancement system to achieve product customization [J]. Industrial Management&Data Systems,2005,105(3-4):384-406.
[201]Leung, R.W.K. Generic methodology for the design and development of an intelligent optimization system[M]. MPhil dissertation, Univ. of Polytechnic, Hong Kong,2002:34-56.
[202]Deng P.S. Tsacle E.G. Coupling genetic algorithms and rule-based systems for complex decisions[J].Expert Systems with Applications,2000,19:209-218.