数据挖掘在笔记本电脑BTO生产计划中的应用研究
|
摘要
随着移动计算技术的发展,笔记本电脑应用日趋广泛,市场规模也不断扩大。影响笔记本电脑品牌竞争力的两个重要因素:面向客户个性化与需求快速响应成为笔记本电脑制造企业关注的焦点,由此笔记本电脑BTO制造模式应运而生。在实际情况中,由于受制于行业特有的不利状况,笔记本电脑BTO生产是通过一种“推拉”结合的生产模式来实现的,因此生产计划的准确性对该模式的正常运行至关重要。
本文对某笔记本电脑制造企业的实际状况进行了调查,分析了该企业生产计划制定中的难点以及实现方式上的特殊性,提出了一种新思路,即用关联规则数据挖掘方法从企业ERP订单模块与PDM数据中提取有关电脑组件配置选择关联度的信息,辅助生产管理部门制定生产计划的思路,帮助企业改变过分依赖个人经验的状况,制定出更为科学、合理的生产计划。具体的工作有:
1、根据笔记本电脑BTO生产计划的特点,对经典关联规则挖掘算法——Apriori算法进行了分组启发式改进并结合多最小支持度、增量更新改进,最终组合形成了一种综合算法,提高了实用性。
2、设计实现了笔记本电脑组件关联规则挖掘系统,并对数据仓库的建立,数据ECTL以及数据挖掘结果的展示做了详细的阐述。
3、结合实例解释了关联规则挖掘结果对生产计划的作用和意义,并介绍了一种基于马尔柯夫链的预测方法,用于电脑配置关联度变化趋势的预测。
Due to the development of mobile computing technology, notebook computers have been gradually used in diverse areas with an increasing market. What major notebook computer manufacturers concern mostly are two key factors—customized computer-design and immediate response to customer demands, which influence the competitiveness of a notebook computer brand. As a result, the build-to-order model is created. In fact, because of several restraints of the industry, BTO production of notebook computers is carried out with the usage of a "push-pull" mixed model. Therefore, the precision of the production plan is crucial to the operation of this model.
Based on the research of a particular notebook computer manufacturer, this paper analyses the difficulties of formulating a proper production plan and several special considerations required to carry out the plan. The thesis also proposes to find out the relations between computer components from ERP orders module and PDM data of the company, by employing association rules data mining, in order to help relevant departments formulate the production plan. The main research work includes:
1. Regarding the characteristics of the BTO production of notebook computers, this paper suggests improving the classic algorithm of association rules data mining—Apriori algorithm, with multi minimum supports, incremental renewal and heuristic grouping, to enhance the practicality in its implementation.
2. This paper explains how to build the data warehouse, the ECTL processing and the user interface in designing and implementation of association rules data mining system.
3. The effects of association rules data mining results to production planning are explained with examples. A method based on the Markov chain is proposed to predict the changes of computer configuration relations.
本文对某笔记本电脑制造企业的实际状况进行了调查,分析了该企业生产计划制定中的难点以及实现方式上的特殊性,提出了一种新思路,即用关联规则数据挖掘方法从企业ERP订单模块与PDM数据中提取有关电脑组件配置选择关联度的信息,辅助生产管理部门制定生产计划的思路,帮助企业改变过分依赖个人经验的状况,制定出更为科学、合理的生产计划。具体的工作有:
1、根据笔记本电脑BTO生产计划的特点,对经典关联规则挖掘算法——Apriori算法进行了分组启发式改进并结合多最小支持度、增量更新改进,最终组合形成了一种综合算法,提高了实用性。
2、设计实现了笔记本电脑组件关联规则挖掘系统,并对数据仓库的建立,数据ECTL以及数据挖掘结果的展示做了详细的阐述。
3、结合实例解释了关联规则挖掘结果对生产计划的作用和意义,并介绍了一种基于马尔柯夫链的预测方法,用于电脑配置关联度变化趋势的预测。
Due to the development of mobile computing technology, notebook computers have been gradually used in diverse areas with an increasing market. What major notebook computer manufacturers concern mostly are two key factors—customized computer-design and immediate response to customer demands, which influence the competitiveness of a notebook computer brand. As a result, the build-to-order model is created. In fact, because of several restraints of the industry, BTO production of notebook computers is carried out with the usage of a "push-pull" mixed model. Therefore, the precision of the production plan is crucial to the operation of this model.
Based on the research of a particular notebook computer manufacturer, this paper analyses the difficulties of formulating a proper production plan and several special considerations required to carry out the plan. The thesis also proposes to find out the relations between computer components from ERP orders module and PDM data of the company, by employing association rules data mining, in order to help relevant departments formulate the production plan. The main research work includes:
1. Regarding the characteristics of the BTO production of notebook computers, this paper suggests improving the classic algorithm of association rules data mining—Apriori algorithm, with multi minimum supports, incremental renewal and heuristic grouping, to enhance the practicality in its implementation.
2. This paper explains how to build the data warehouse, the ECTL processing and the user interface in designing and implementation of association rules data mining system.
3. The effects of association rules data mining results to production planning are explained with examples. A method based on the Markov chain is proposed to predict the changes of computer configuration relations.
引文
1 赛迪网.2006-2007中国笔记本电脑产业研究报告[EB/OL].http://market.ccidnet.com/report/content/7/200706/23508.html
2 许一敏,马士华.基于客户订单的制造业企业BOSC模式[J].物流技术,2007,(02):192-193,208
[1]A.Gunasekaran. The build-to-order supply chain (BOSC): a competitive strategy for 21st century[J]. Journal of Operations Management, 2005,23(5): 419-422
[2]A.Gunasekaran & E.W.T.Ngai. Build-to-order supply chain management: a literature review and framework for development[J]. Journal of Operations Management, 2005,23(5): 423-451
[3]A.Savasere, E.Omiecinski & S.Navathe. An efficient algorithm for mining association rules in large databases[A]. Proceedings of the International Very Large Database Conference[C], 1995: 432-444
[4]A.Savasere, E.Omiecinski & S.Navathe. Mining for Strong Negative Associations in A Large Database of Customer Transactions [A]. Proceedings of the 14~(th) International Conference on Data Engineering (ICDE'98)[C], Orlando, Florida, IEEE Computer Society Press Publisher, 1998: 494-502
[5]B.Ozden, S.Ramaswamy & A.Silberschatz. Cyclic Association Rules[A]. Proceedings of the 14~(th) International Conference on Data Engineering(ICDE'98) [C], Orlando, Florida, IEEE Computer Society Press Publisher, 1998: 412-421
[6]Brachman R J & Anand T. The Process of Knowledge Discovery in Databases[M]. A Human-centered Approach. Adavance In Knowledge Discovery And Data Mining. AAAIIMIT Press, 1996:37-58
[7]Chen Guoqing, Wei Qiang & Liu De, et al. Simple association rules (SAR) and the SAR-based rule discovery[J]. Computers and Industrial Engineering, 2002, 43(4): 721-733
[8]Chen M S, Han J & Yu P S. Data Mining: An Overview from a Database Perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 866-883
[9]Cheung D, Han J, Ng V & Wong C Y. Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique[A]. Proc.of International Conference on Data Engineering (ICDE'96)[C], New Orleans, USA, 1996:106-114
[10]David Wai-Lok Cheung, Jiawei Han & Vincent Ng, et al. A Fast Distributed Algorithm for Mining Association Rules[A]. Proceedings of the Fourth International Conference on Parallel and Distributed Information Systems (PDIS'96)[C], Miami Beach, Florida, IEEE Computer Society Publisher, 1996: 31-42
[11]H. Toivonen. Sampling large databases for association rules[A]. Proceedings of the International Very Large Databases Conference[C], 1996: 134-145
[12]J Griffiths & D Margetts. Variation in Production Schedules-implica-tions for both the Company and its Suppliers[J]. Journal of Materials Processing Technology, 2000, (103): 155-159
[13]J Han, J Pei & Y Yin. Mining Frequent Patterns Without Candidate Generation[A]. In Proc, of the 2000 ACM-SIGMOD Int'l Conf, on Management of Data[C], Dallas, Texas, USA, 2000: 1-12
[14]Jong Soo Park, Ming Syan Chen & Philip S.Yu. Efficient Parallel and Data Mining for Association Rules[A]. Proceedings of the 4~(th) International Conference on Information and Knowledge Management (CIKM'95)[C], Baltimore, Maryland, ACM Press Publisher, 1995: 31-36
[15]K.Koperski & J.Han. Discovery of Spatial Association Rules in Geographic Information Databases[A]. Max J.Egenhofer and John R.Herring. Proceedings of the 4~(th) International Symposium on Large Spatial Databases(SSD'95)[C], Portland, Maine, Springer Publisher, 1995: 47-66
[16]N.Pasquier, Y.Bastide, R.Taouil & L.Lakhal. Discovering frequent closed itemsets for association rules[A]. C.Beeri and P.Buneman. Proceedings of the 7~(th) International Conference on Database Theory (ICDT'99)[C], Jerusalem,Israel, Springer Publisher, 1999: 398-416
[17]Park J S. An Effective Hash Based Algorithm for Mining Association Rules [A]. Proceedings of ACM SIGMOD[C], 1995: 175-186
[18]Piatetsky-Shapiro.G & Frawley.W.J. Knowledge Discovery in Database[M], AAAI/MIT Press, 1991
[19]R.Agrawal & J.C.Shafer. Parallel Mining of Association Rules: Design, Implementation and Experience [J]. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 962-969
[20]R Agrawal & R Srikant. Fast algorithms for mining association rules [A]. Proc 20th Int'l Conf. Very Large Database [C]. Santiago, Chile, Sept 1994: 487-499
[21]R.Agrawal & R.Srikant. Mining sequential patterns[A]. Philip S.Yu and Arbee S.P.Chen. Proceedings of the 11~(th) International Conference on Data Engineering(ICDE'95)[C], Taipei, Taiwan, IEEE Computer Society Press Publisher, 1995: 3-14
[22]R Agrawal, T Imielinski & A Swami. Mining Association Rules between Sets of Items in Large Databases [A]. In Proc, of the ACM SIGMOD Int' I Conf, on Management of Data(ACM SIGMOD'93)[C],Washington,USA,1993:207-216
[23]R.J.Bayardo.Efficiently mining long patterns from databases[A].Laura M.Haas and Ashutosh Tiwary.Proceedings of the 1998 ACM-SIGMOD International Conference On Management of Data(SIGMOD'98)[C],Seattle,Washington,ACM Press Publisher,1998:85-93
[24]S.Ramaswamy,S.Mahajan & A.Silberschatz.On the Discovery of Interesting Patterns in Association Rules[A].Ashish Gupta,Oded Shmueli & Jennifer Widom.Proceedings of the 24~(th)International Conference on Very Large Databases(VLDB'98)[C],New York City,New York,Morgan Kaufmann Publisher,1998:368-379
[25]Sudipto Guha,Rajeev Rastogi & Kyuseok Shim.ROCK:A Robust Clustering Algorithm for Categorical Attributes[A].Proceedings of the 15~(th)International Conference on Data Engineering(ICDE'99)[C],Sydney,Australia,IEEE Computer Society Press Publisher,1999:512-521
[26]U.M.Fayyad,G.P.Shapiro,& P.Smyth,et al.Advances in Knowledge Discovery and Data Mining[M].AAAI/MIT Press,1996
[27]W.H.Inmon.Dawn of a new age:why everyone is building a data warehouse[J].Database programming and Design,1992,5(12):76-77
[28]Yingdong Lu.Performance analysis for assemble-to-order systems with general renewal arrivals and random batch demands[J].European Journal of Operational Research,2008,(185):635-647
[29]戴宝纯,张宇孝.“按订单”生产模式下生产计划的编制[J].制造业自动化,2000,(9):7-11
[30]冯玉才,冯剑琳.关联规则的增量式更新算法[J].软件学报,1998,9(1):301-306
[31]宫雨.分组多支持度关联规则研究[J].计算机工程与设计,2007,(5):1205-1207
[32]桂华明.基于需求管理的企业生产计划模式研究[J].物流技术,2006,(7):202-205
[33]贺胜柏.马尔柯夫预测模型及其实证分析[J].中南财经政法大学学报,2002,(2):132-135
[34]何炎祥,石莉,张戈等.关联规则的几种开采算法及其比较分析[J].小型微型计算机系统,2001,22(9):1065-1068
[35]胡侃,夏绍玮.基于大型数据仓库的数据挖掘[J].软件学报,1998,9(1):61-63
[36]黄进.关联规则数据挖掘算法的研究[D].硕士学位论文.西南交通大学,2003
[37]姜大维.按单定制供应链(BOSC)-戴尔公司如何实施BOSC?[J].软科学,2006,(4):127-130
[38]姜迎春,张安民,宋学平.订单式中小企业生产计划管理系统研究[J].机械,2005,(9):60-63
[39]Jiawei Han,Micheline Kamber著,范明,孟小峰等译.数据挖掘概念与技术[M].北京:机械工业出版社,2001
[40]李世明.基于关联规则算法的垃圾邮件过滤研究[J].科技咨询导报,2007,(19):11-12
[41]刘思峰,党耀国.预测与决策[M].北京:高等教育出版社,2005
[42]路松峰,卢正鼎.快速开采最大频繁项目集[J].软件学报,2001,12(2):293-297
[43]马士华,林勇.供应链管理[M].北京:高等教育出版社,2003
[44]彭祖成,郭刚,金莉.主生产计划的可行性问题研究[J].成组技术与生产现代化,2005,(3):24-27
[45]赛迪网.2006-2007 中国笔记本电脑产业研究报告[EB/OL].http://market.ccidnet.com/report/content/7/200706/23508.html,2007-5-25
[46]宋余庆,朱玉全等.基于FP-Tree的最大频繁项目集挖掘及更新算法[J].软件学报,2003,14(9):1586-1592
[47]王巧梅.基于需求管理企业生产计划的制定[J].陕西煤炭,2007,(3):80-81
[48]王彧,马士华.基于缩短供应链响应周期的运作模式研究[J].工业工程与管理,2004,(1):51-52
[49]徐辉.面向订单的柔性生产计划研究及应用[D].硕士学位论文,浙江大学,2004
[50]徐文拴,辛运帏.一种新的基于FP-Tree的关联规则增量式更新算法[J].计算机工程与设计,2006,(18):3430-3432
[51]许一敏,马士华.基于客户订单的制造业企业BOSC模式[J].物流技术,2007,(2):192-193,208
[52]叶红朝,孙毅.基于双阶分离式BOM的物料需求计划算法的研究与实现[J].成组技术与生产现代化,2006,(4):53-56
[53]袁新生.LINGO和Excel在数学建模中的应用.北京:科学出版社,2007
[54]张基温,魏士靖.关联规则技术在计算机犯罪取证中的应用[J].微计算机应用,2007,(7):776-779
[55]章兢,张小刚.数据挖掘算法及其工程应用[M].北京:机械工业出版社,2006
[56]章志明,黄龙军,余敏等.一种动态的频繁项集挖掘算法[J].计算机工程,2006,(24):78-80
[57]钟卫.马尔柯夫转移概率矩阵的估计及其在市场占有率预测中的应用[J].统计与预测,2001,(2):35-36
[58]周根贵.数据仓库与数据挖掘[M].杭州:浙江大学出版社,2004
2 许一敏,马士华.基于客户订单的制造业企业BOSC模式[J].物流技术,2007,(02):192-193,208
[1]A.Gunasekaran. The build-to-order supply chain (BOSC): a competitive strategy for 21st century[J]. Journal of Operations Management, 2005,23(5): 419-422
[2]A.Gunasekaran & E.W.T.Ngai. Build-to-order supply chain management: a literature review and framework for development[J]. Journal of Operations Management, 2005,23(5): 423-451
[3]A.Savasere, E.Omiecinski & S.Navathe. An efficient algorithm for mining association rules in large databases[A]. Proceedings of the International Very Large Database Conference[C], 1995: 432-444
[4]A.Savasere, E.Omiecinski & S.Navathe. Mining for Strong Negative Associations in A Large Database of Customer Transactions [A]. Proceedings of the 14~(th) International Conference on Data Engineering (ICDE'98)[C], Orlando, Florida, IEEE Computer Society Press Publisher, 1998: 494-502
[5]B.Ozden, S.Ramaswamy & A.Silberschatz. Cyclic Association Rules[A]. Proceedings of the 14~(th) International Conference on Data Engineering(ICDE'98) [C], Orlando, Florida, IEEE Computer Society Press Publisher, 1998: 412-421
[6]Brachman R J & Anand T. The Process of Knowledge Discovery in Databases[M]. A Human-centered Approach. Adavance In Knowledge Discovery And Data Mining. AAAIIMIT Press, 1996:37-58
[7]Chen Guoqing, Wei Qiang & Liu De, et al. Simple association rules (SAR) and the SAR-based rule discovery[J]. Computers and Industrial Engineering, 2002, 43(4): 721-733
[8]Chen M S, Han J & Yu P S. Data Mining: An Overview from a Database Perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 866-883
[9]Cheung D, Han J, Ng V & Wong C Y. Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique[A]. Proc.of International Conference on Data Engineering (ICDE'96)[C], New Orleans, USA, 1996:106-114
[10]David Wai-Lok Cheung, Jiawei Han & Vincent Ng, et al. A Fast Distributed Algorithm for Mining Association Rules[A]. Proceedings of the Fourth International Conference on Parallel and Distributed Information Systems (PDIS'96)[C], Miami Beach, Florida, IEEE Computer Society Publisher, 1996: 31-42
[11]H. Toivonen. Sampling large databases for association rules[A]. Proceedings of the International Very Large Databases Conference[C], 1996: 134-145
[12]J Griffiths & D Margetts. Variation in Production Schedules-implica-tions for both the Company and its Suppliers[J]. Journal of Materials Processing Technology, 2000, (103): 155-159
[13]J Han, J Pei & Y Yin. Mining Frequent Patterns Without Candidate Generation[A]. In Proc, of the 2000 ACM-SIGMOD Int'l Conf, on Management of Data[C], Dallas, Texas, USA, 2000: 1-12
[14]Jong Soo Park, Ming Syan Chen & Philip S.Yu. Efficient Parallel and Data Mining for Association Rules[A]. Proceedings of the 4~(th) International Conference on Information and Knowledge Management (CIKM'95)[C], Baltimore, Maryland, ACM Press Publisher, 1995: 31-36
[15]K.Koperski & J.Han. Discovery of Spatial Association Rules in Geographic Information Databases[A]. Max J.Egenhofer and John R.Herring. Proceedings of the 4~(th) International Symposium on Large Spatial Databases(SSD'95)[C], Portland, Maine, Springer Publisher, 1995: 47-66
[16]N.Pasquier, Y.Bastide, R.Taouil & L.Lakhal. Discovering frequent closed itemsets for association rules[A]. C.Beeri and P.Buneman. Proceedings of the 7~(th) International Conference on Database Theory (ICDT'99)[C], Jerusalem,Israel, Springer Publisher, 1999: 398-416
[17]Park J S. An Effective Hash Based Algorithm for Mining Association Rules [A]. Proceedings of ACM SIGMOD[C], 1995: 175-186
[18]Piatetsky-Shapiro.G & Frawley.W.J. Knowledge Discovery in Database[M], AAAI/MIT Press, 1991
[19]R.Agrawal & J.C.Shafer. Parallel Mining of Association Rules: Design, Implementation and Experience [J]. IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 962-969
[20]R Agrawal & R Srikant. Fast algorithms for mining association rules [A]. Proc 20th Int'l Conf. Very Large Database [C]. Santiago, Chile, Sept 1994: 487-499
[21]R.Agrawal & R.Srikant. Mining sequential patterns[A]. Philip S.Yu and Arbee S.P.Chen. Proceedings of the 11~(th) International Conference on Data Engineering(ICDE'95)[C], Taipei, Taiwan, IEEE Computer Society Press Publisher, 1995: 3-14
[22]R Agrawal, T Imielinski & A Swami. Mining Association Rules between Sets of Items in Large Databases [A]. In Proc, of the ACM SIGMOD Int' I Conf, on Management of Data(ACM SIGMOD'93)[C],Washington,USA,1993:207-216
[23]R.J.Bayardo.Efficiently mining long patterns from databases[A].Laura M.Haas and Ashutosh Tiwary.Proceedings of the 1998 ACM-SIGMOD International Conference On Management of Data(SIGMOD'98)[C],Seattle,Washington,ACM Press Publisher,1998:85-93
[24]S.Ramaswamy,S.Mahajan & A.Silberschatz.On the Discovery of Interesting Patterns in Association Rules[A].Ashish Gupta,Oded Shmueli & Jennifer Widom.Proceedings of the 24~(th)International Conference on Very Large Databases(VLDB'98)[C],New York City,New York,Morgan Kaufmann Publisher,1998:368-379
[25]Sudipto Guha,Rajeev Rastogi & Kyuseok Shim.ROCK:A Robust Clustering Algorithm for Categorical Attributes[A].Proceedings of the 15~(th)International Conference on Data Engineering(ICDE'99)[C],Sydney,Australia,IEEE Computer Society Press Publisher,1999:512-521
[26]U.M.Fayyad,G.P.Shapiro,& P.Smyth,et al.Advances in Knowledge Discovery and Data Mining[M].AAAI/MIT Press,1996
[27]W.H.Inmon.Dawn of a new age:why everyone is building a data warehouse[J].Database programming and Design,1992,5(12):76-77
[28]Yingdong Lu.Performance analysis for assemble-to-order systems with general renewal arrivals and random batch demands[J].European Journal of Operational Research,2008,(185):635-647
[29]戴宝纯,张宇孝.“按订单”生产模式下生产计划的编制[J].制造业自动化,2000,(9):7-11
[30]冯玉才,冯剑琳.关联规则的增量式更新算法[J].软件学报,1998,9(1):301-306
[31]宫雨.分组多支持度关联规则研究[J].计算机工程与设计,2007,(5):1205-1207
[32]桂华明.基于需求管理的企业生产计划模式研究[J].物流技术,2006,(7):202-205
[33]贺胜柏.马尔柯夫预测模型及其实证分析[J].中南财经政法大学学报,2002,(2):132-135
[34]何炎祥,石莉,张戈等.关联规则的几种开采算法及其比较分析[J].小型微型计算机系统,2001,22(9):1065-1068
[35]胡侃,夏绍玮.基于大型数据仓库的数据挖掘[J].软件学报,1998,9(1):61-63
[36]黄进.关联规则数据挖掘算法的研究[D].硕士学位论文.西南交通大学,2003
[37]姜大维.按单定制供应链(BOSC)-戴尔公司如何实施BOSC?[J].软科学,2006,(4):127-130
[38]姜迎春,张安民,宋学平.订单式中小企业生产计划管理系统研究[J].机械,2005,(9):60-63
[39]Jiawei Han,Micheline Kamber著,范明,孟小峰等译.数据挖掘概念与技术[M].北京:机械工业出版社,2001
[40]李世明.基于关联规则算法的垃圾邮件过滤研究[J].科技咨询导报,2007,(19):11-12
[41]刘思峰,党耀国.预测与决策[M].北京:高等教育出版社,2005
[42]路松峰,卢正鼎.快速开采最大频繁项目集[J].软件学报,2001,12(2):293-297
[43]马士华,林勇.供应链管理[M].北京:高等教育出版社,2003
[44]彭祖成,郭刚,金莉.主生产计划的可行性问题研究[J].成组技术与生产现代化,2005,(3):24-27
[45]赛迪网.2006-2007 中国笔记本电脑产业研究报告[EB/OL].http://market.ccidnet.com/report/content/7/200706/23508.html,2007-5-25
[46]宋余庆,朱玉全等.基于FP-Tree的最大频繁项目集挖掘及更新算法[J].软件学报,2003,14(9):1586-1592
[47]王巧梅.基于需求管理企业生产计划的制定[J].陕西煤炭,2007,(3):80-81
[48]王彧,马士华.基于缩短供应链响应周期的运作模式研究[J].工业工程与管理,2004,(1):51-52
[49]徐辉.面向订单的柔性生产计划研究及应用[D].硕士学位论文,浙江大学,2004
[50]徐文拴,辛运帏.一种新的基于FP-Tree的关联规则增量式更新算法[J].计算机工程与设计,2006,(18):3430-3432
[51]许一敏,马士华.基于客户订单的制造业企业BOSC模式[J].物流技术,2007,(2):192-193,208
[52]叶红朝,孙毅.基于双阶分离式BOM的物料需求计划算法的研究与实现[J].成组技术与生产现代化,2006,(4):53-56
[53]袁新生.LINGO和Excel在数学建模中的应用.北京:科学出版社,2007
[54]张基温,魏士靖.关联规则技术在计算机犯罪取证中的应用[J].微计算机应用,2007,(7):776-779
[55]章兢,张小刚.数据挖掘算法及其工程应用[M].北京:机械工业出版社,2006
[56]章志明,黄龙军,余敏等.一种动态的频繁项集挖掘算法[J].计算机工程,2006,(24):78-80
[57]钟卫.马尔柯夫转移概率矩阵的估计及其在市场占有率预测中的应用[J].统计与预测,2001,(2):35-36
[58]周根贵.数据仓库与数据挖掘[M].杭州:浙江大学出版社,2004