领域驱动知识发现方法研究
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摘要
数据挖掘是一种常用的、从海量数据中获得潜在的、有用知识的发现过程,但传统的数据挖掘是以数据为驱动,强调挖掘过程的自动化,挖掘的结果中常常包括大量冗余、甚至错误的知识,无法直接应用于现实世界的实践活动。以挖掘用户感兴趣,可行动知识为目标的数据挖掘,需要将领域知识,特别是专家的偏好、经验、知识和智慧贯穿于整个挖掘数据过程中,变数据驱动的数据挖掘为领域驱动的数据挖掘,以弥补数据挖掘在学术研究与现实世界应用之间存在的鸿沟。另一方面,大量知识存在于人类载体中,特别是具有丰富理论与实践经验的领域专家,对于复杂问题的求解需要直接以领域专家为挖掘对象,获得群体专家对复杂问题的共识知识,并通过两种知识发现方式的相互补充、验证,从而能获更为全面、准确的知识。
本文应用管理科学、计算机科学及综合集成方法论,以领域知识为驱动、数据与领域专家为挖掘对象研究获取用户感兴趣、可行动知识的方法,并将这种方法称为领域驱动知识发现,主要的研究内容包括：
1、在分析传统数据挖掘存在不足的基础上,研究如何将领域知识贯穿到整个数据过程的方法,进一步丰富、充实领域驱动数据挖掘的相关理论。针对传统数据挖掘过程模型CRISP-DM存在的不足,提出一个新的领域驱动数据挖掘过程模型,并引入综合集成系统方法论指导领域驱动数据挖掘过程。
2、提出一个基于语义的Apriori改进算法以实现将领域知识整合到挖掘算法中,以满足获取不同层次、不同目的的挖掘需求。
3、研究如何在领域专家研讨获得专家的共识知识。在分析专家知识特点的基础上,建立专家知识模型,在对专家共识分析时,提出用对应分析方法从专家与专家意见两个维度同时进行聚类并在二维平面上进行映射,以挖掘专家之间,专家与意见之间的聚类知识,同时改进一个二分图网络投影压缩算法,并将其应用于计算专家的意见相似度中,以定量化方法描述专家意见的相似性和独立性。
4、在上述理论研究的基础上,设计开发一个以领域知识为驱动知识发现平台,利用该平台分别从数据和专家两种知识载体为对象挖掘名老中医的学术思想,实证结果表明领域驱动知识发现方法的可行性及优势。
Data mining is a common knowledge discovery process which taps potential and useful knowledge from massive data.However, traditional data mining is data-driven, emphasizing automation in data mining process, therefore the mining results often include a lot of redundant, even wrong knowledge which can not be directly applied to real world business activities. Data mining method which aims at mining users'interests and actionable knowledge should embrace domain knowledge, especially experts'preferences, experiences, knowledge and wisdom throughout the mining process, transforming data-driven data mining into domain-driven data mining, so as to bridge the gap between academic research and the real-world application. On the other hand, a lot of knowledge stores in human minds, especially those experts with rich theoretical and practical experiences. Therefore, in order to solve complex problems, experts should be regarded as direct mining targets. In this way, experts'knowledge on complex issues can be obtained, and through the combination of two knowledge discovery modes, we can get more comprehensive and accurate knowledge.
With management science, computer science and meta-synthesis as the basis, interesting and actionable knowledge gained from both data and experts is regarded as Domain-Driven Knowledge Discovery, the tasks of this dissertation include:
1.Based on analyzing the shortcomings of traditional data mining, it studies theories and methods on how to integrate domain knowledge into the whole data mining process, so as to further enrich the content of the domain-driven data mining theories. As to the deficiencies of traditional data mining model (CRISP-DM), this thesis proposes a new Domain-Driven Data Mining model and introduces the methodology of comprehensive integrated system as the guidance of Domain-Driven Data Mining process.
2.It also proposes a semantic Apriori algorithm which integrates domain knowledge into the data mining algorithm in order to meet demands of different levels and various mining purposes.Then, the thesis studies how to get consensus of experts during discussion.
3.After analyzing the characteristics of expert knowledge, it builds an expert knowledge model, using correspondence analysis to conduct clustering in both expert and experts'views dimensions simultaneously and mapping on two-dimensional plane in order to tap knowledge among experts, as well as knowledge between experts and experts'views.Meanwhile, a weighted bipartite network projection algorithm is applied to calculate similarity of the experts'opinions, so as to describe similarity and independence of experts'opinions in a quantitative way.
4.Finally, this thesis designs and develops a domain knowledge-driven knowledge discovery platform, and applies the platform to tap academic thoughts of Chinese veteran practioners of TCM from data as well as from experts.The empirical result shows feasibility and advantages of domain-driven knowledge discovery.
本文应用管理科学、计算机科学及综合集成方法论,以领域知识为驱动、数据与领域专家为挖掘对象研究获取用户感兴趣、可行动知识的方法,并将这种方法称为领域驱动知识发现,主要的研究内容包括：
1、在分析传统数据挖掘存在不足的基础上,研究如何将领域知识贯穿到整个数据过程的方法,进一步丰富、充实领域驱动数据挖掘的相关理论。针对传统数据挖掘过程模型CRISP-DM存在的不足,提出一个新的领域驱动数据挖掘过程模型,并引入综合集成系统方法论指导领域驱动数据挖掘过程。
2、提出一个基于语义的Apriori改进算法以实现将领域知识整合到挖掘算法中,以满足获取不同层次、不同目的的挖掘需求。
3、研究如何在领域专家研讨获得专家的共识知识。在分析专家知识特点的基础上,建立专家知识模型,在对专家共识分析时,提出用对应分析方法从专家与专家意见两个维度同时进行聚类并在二维平面上进行映射,以挖掘专家之间,专家与意见之间的聚类知识,同时改进一个二分图网络投影压缩算法,并将其应用于计算专家的意见相似度中,以定量化方法描述专家意见的相似性和独立性。
4、在上述理论研究的基础上,设计开发一个以领域知识为驱动知识发现平台,利用该平台分别从数据和专家两种知识载体为对象挖掘名老中医的学术思想,实证结果表明领域驱动知识发现方法的可行性及优势。
Data mining is a common knowledge discovery process which taps potential and useful knowledge from massive data.However, traditional data mining is data-driven, emphasizing automation in data mining process, therefore the mining results often include a lot of redundant, even wrong knowledge which can not be directly applied to real world business activities. Data mining method which aims at mining users'interests and actionable knowledge should embrace domain knowledge, especially experts'preferences, experiences, knowledge and wisdom throughout the mining process, transforming data-driven data mining into domain-driven data mining, so as to bridge the gap between academic research and the real-world application. On the other hand, a lot of knowledge stores in human minds, especially those experts with rich theoretical and practical experiences. Therefore, in order to solve complex problems, experts should be regarded as direct mining targets. In this way, experts'knowledge on complex issues can be obtained, and through the combination of two knowledge discovery modes, we can get more comprehensive and accurate knowledge.
With management science, computer science and meta-synthesis as the basis, interesting and actionable knowledge gained from both data and experts is regarded as Domain-Driven Knowledge Discovery, the tasks of this dissertation include:
1.Based on analyzing the shortcomings of traditional data mining, it studies theories and methods on how to integrate domain knowledge into the whole data mining process, so as to further enrich the content of the domain-driven data mining theories. As to the deficiencies of traditional data mining model (CRISP-DM), this thesis proposes a new Domain-Driven Data Mining model and introduces the methodology of comprehensive integrated system as the guidance of Domain-Driven Data Mining process.
2.It also proposes a semantic Apriori algorithm which integrates domain knowledge into the data mining algorithm in order to meet demands of different levels and various mining purposes.Then, the thesis studies how to get consensus of experts during discussion.
3.After analyzing the characteristics of expert knowledge, it builds an expert knowledge model, using correspondence analysis to conduct clustering in both expert and experts'views dimensions simultaneously and mapping on two-dimensional plane in order to tap knowledge among experts, as well as knowledge between experts and experts'views.Meanwhile, a weighted bipartite network projection algorithm is applied to calculate similarity of the experts'opinions, so as to describe similarity and independence of experts'opinions in a quantitative way.
4.Finally, this thesis designs and develops a domain knowledge-driven knowledge discovery platform, and applies the platform to tap academic thoughts of Chinese veteran practioners of TCM from data as well as from experts.The empirical result shows feasibility and advantages of domain-driven knowledge discovery.
引文
[1]王众托.知识系统工程.北京：科学出版社,2004.
[2]Frawley W, Piatetsky-Shapiro G, Matheus C. Knowledge Discovery in Databases:An Overview. AI Magazine,1992(13):213-228.
[3]Cao L B, Zhang C Q. Domain-Driven Actionable Knowledge Discovery in the Real World. PAKDD 2006,LNAI3918,821-830.
[4]Cao L B, Rick Schurmann R, Zhang C. Q. Domain-Driven In-Depth Pattern Discovery:A Practical Methodology. Australian Data Mining Conf.,2005.
[5]Silberschatz A, Tuzhilin A. What makes patterns interesting in Knowledge Discovery System. IEEE Trans,Knowledge and Data Engineering, Dec.1996:970-974.
[6]Shi Y, Li XS.Knowledge Management Platforms and Intelligent Knowledge beyond Data Mining. Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom, Y. Shi et al.(Eds.),IOS Press,2007:272-288.
[7]Yoon S C,Lawrence J, He,Park E K, Sam M. Using Domain Knowledge in Knowledge Discovery, Proceedings of the eighth international conference on Information and knowledge managemen,1999:243-250.
[8]Theresa I J, Thomas J. N. A domain-driven approach to improving search effectivenessin traditional online catalogsTheresa. Information & Management,2002:559-570.
[9]Sergei K,David H L. Domain Driven Web Development With Webjinn. OOPSLA'03,2003: 53-63.
[10]Fan J H, Li D Y. An overview of Data Mining and Knowledge Discovery. Journal of Computer Science and Technology,1998.
[11]Cross Industry Standard Process for Data Mining.http://www.crisp-dm. org,1999
[12]Agrawal R, Imielinski T, Swami A. Database mining:A performance perspective.IEEE Transactions on Knowledge and Data Engineering,1993:914-925.
[13]Agrawal R, Srikant R.Fast algorithm for mining association Rules in large databases[A].The International Conference on Very Large Data Bases.1994:487-499.
[14]Han J,Pei J,Yin Y. Ming frequent patterns without candidate generation.In Proc.2000ACM-SIGMOD Int.Conf.Management of Data(SIGMOD'00).Dallas,TX,2000: 1-12.
[15]Quinlan J R.Induction of decision trees. Machine Leaming,1986(1):81-106.
[16]Schlimmer J C,Fisher D.A case study of incremental concept induction.In Proceedings of AAAI-86[C].1986.
[17]Quinlan J R.C4.5:Programs for machine learning[M].San Mateo,California:Morgan Kaufmann,1993.
[18]Shi Y. Peng y,Xu W, Tang X. Data mining Via multiple criteria linear programming: applications in credit card protfolio management.International Journal of Information Technology and Decision Making.2002:131-151.
[19]Mehta M,Agrawal R,Rissanen J.SLIQ:A fast scalable classifier for data mining.Lecture notes in computer sciProc of the 5th int conf on extending database Tech.Avignon, France,1996:18-33.
[20]Shafer J C,Agrawal R,Mehta M.SPRINT:A scalable parallel classifier for data mining.Proc of the 22nd Int conf on very large databases.Mumbai(Bombay),India, 1996.
[21]Gehrke J,Ramakrishnan R,Ganti V. Rainforest a framework for fast decision tree construction of large datasets[A].In VLDB.1998.
[22]Friedman N,Geiger D,Goldszmidt M.Bayesian network classitier.Machine Learning, 1997,29(1):131-163.
[23]Liu B,Hsu M a Integrating classification and association rule mining.Proc ofthe 4th int conf on knowledge discovery and Data Mining.NUSA:AAAI Press,1998:80-86.
[24]Kovalerchuk B, Evgen V,James F R, Consistent and complete data and expert mining in medicine.in Medical Data Mining and Knowledge Discovery, Berlin:springer, 2001:238-280.
[25]Cortes C, Vapnik V.:Support vector networks, Machine learning,1995,20:273.
[26]MacQueen J. Some Methods for Classification-and Analysis of Multivariate Observations.Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability,1967.
[27]Ng R,Han J. CLARANS:A method for clustering objects for spatial datamining.IEEE Trans on Knowl,DataEng,2002,14(5):1003-1016.
[28]Zhang Tian, Ramakrishnan R, Livny M. BIRCH:An efficient data clustering method for very large databases.ACM SIGMOD Record,1996,25(2):103-114.
[29]Guha S, Rastogi R, Shim K. Cure:An Efficient Clustering Algorithm for Large Database.Proc.of ACM-SIGMOND Int.Conf. Management on Data, Seattle, Washington,1998: 73-84.
[30]Wang W, Yang J, Muntz R. STING:A Statistical Information Grid Approach to Spatial Data Mining. Proc.of the 23th International Conference on Very Large Data Bases,1997.
[31]Agrawal R, Gehrke J, Gunopulcs D. Automatic Subspace Clustering of High Dimensional Data for Data Mining Application. Proc.of ACM SIGMOD Intconfon Management on Data, Seattle,WA,1998:94-205.
[32]Hernandez S S. The merge purge problem for large database. Proceedings of the 1995 ACM SIGMOD international Conference on Management of Data, New York.USA:ACM press,1995:127-138.
[33]Gediminas A, Alexander T. Expert-Driven Validation of Rule-Based User Models in Personalization Applications.Data Mining and Knowledge Discovery,2001(5):33-58.
[34]Mihael A, Human Involvement and Interactivity of the Next Generation',s Data Mining Tools,CM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Santa Barbara, CA,2001.
[35]Siberschatz A, Tuzhilin A. What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Transactions on Knowledge and Data Engineering, Vol.8, No.6,1996: 970-974.
[36]Harleen. K, Actionable Rules:Issues and New Directions, Proceedings of world academy of science, Engineering and Technology,2005(5):1307-6884.
[37]Dragan G, Nada L. Generating Actionable Knowledge by Expert-Guided Subgroup Discovery,PKDD,2002:163-175.
[38]Gediminas A, Alexander T. Discovery of Actionable Patterns in Databases:The Action Hierarchy Approach. KDD'97,1997.
[39]Seunghyun I,Zbigniew W. Action Rule Extraction from a Decision Table: ARED.Springer-Verlag Berlin Heidelberg, LNAI4994,2008:160-168.
[40]Klaus J,Marc D. Mining Intrusion Detection Alarms for Actionable Knowledge.SIGKDD '02,2002:366-375.
[41]张玲玲,李军,石勇.基于数据挖掘的智能知识管理研究.中国管理科学,2009(15)：6-13.
[42]Song T K, Andrew L, Liz S.Domain Ontology Driven Data Mining,2007 ACM SIGKDD Workshop on Domain Driven Data Mining,2007:11-17.
[43]Song H,MA F y,LIU X Q. Ontology-based Knowledge Extraction from Hidden Web.Journal of Donghua University.2004(5):73-78.
[44]Pan D,SHEN D Y,ZHOU M X. Incorporating Domain Knowledge into Data Mining Process: An Ontology Based Framework. Wuhan University journal of Natural Science,2006 (1):165-169.
[45]Li Y H, Lu Z D, Sun X L, elt. Data Mining Ontology Development for High User Usability.Wuhan University Journal of Natural Sciences.2006(1):51-56.
[46]易国洪,章瑾.基于本体的数据挖掘方法研究.计算机与数字工程,2007(7)：42-44.
[47]Anthony K. H. Tungl, Jiawei Han,elt Constraint-Based Clustering in Large Databases.Springer-Verlag Berlin Heidelberg,ICDT 2001:405-419.
[48]Han J, Lakshmanan L V. S,Ng R T. Constraint-Based, Multidimensional Data Mining, Computer(special issues on Data Mining),1999,32(8):46-50.
[49]Juan M D,Paloma D, el at.A multi-Agent Approach to Colaborative Knowledge Production. Intelligent Agents for Data Mining and Information Retrieval,Idea Group Publishing 2004:42-53.
[50]Samhaa R E,Ahmed R,Yasser A. Using Dynamically Acquired Background Knowledge for Information extraction and Intelligent Search. Idea Group Publishing 2004:174-183.
[51]Andreevskaia A.Abi-Aad R. Radhakrishnan T. Agent-Mediated Knowledge Acquisition for User Profiling. Idea Group Publishing 2004:148-157.
[52]Zhu Z X, Song W Q, Gu J F. A Multi-agent and Data Mining Model for TCM Cases Knowledge Discovery,CCCM 2008:341-346.
[53]Zhang C Q, Zhang S C, In-Depth Data Mining and Its Application in Stock Market,Advanced Data Mining and Applications,First International Conference,ADMA 2005,Wuhan, China, July 22-24,2005,Proceedings,2005:13.
[54]Cao L.,Zhang, C.,Yu,P.,et al.Domain-Driven actionable knowledge discovery, IEEE Intelligent Systems,2007,22(4):78-89.
[55]Cao L B. Domain Driven Data Mining:Challenges and Prospects, IEEE Trans.on Knowledge and Data Engineering,2009.
[56]Cao, L.,Zhang, C.:Knowledge Actionability:Satisfying Technical and Business Interestingness,International Journal of Business Intelligence and Data Mining, 2007,2(4):496-514.
[57]Cao, L.,Zhang, C.:The Evolution of KDD:Towards Domain-Driven Data Mining, International Journal of Pattern Recognition and Artificial Intelligence, 2007,21(4):677-692,.
[58]顾基发.意见综合—怎样达成共识.系统工程学报,2001(5)：340-348.
[59]顾基发,王浣尘,唐锡晋.综合集成与复杂系统专辑序.系统工程学报,2001,16(5)：321.
[60]顾基发,唐锡晋.综合集成与知识科学.系统工程理论与实践,2002(10)：2-7.
[61]Kovalerchuk B,Delizy F. Visual data mining using monotone Boolean functions.In:B. Kovalerchuk and J. Schwing, Editors,Visual and spatial analysis,Springer, Dordrecht 2004.
[62]Kovalerchuk B, Vityaev, J. Ruiz.Consistent knowledge discovery in medical diagnosis. IEEE Engineering in Medicine and Biology, (Special issue on Data Mining and Knowledge Discovery),2000:26-37.
[63]Compton P, Horn R, Quinlan R, et al.Maintaining an Expert System in J R Quinlan, Proc. of the 4th Conf. on Application of Expert System.1988.
[64]Kang B, Compton P, et al.Simulated Expert Evaluation of Multiple Classification Ripple Down Rules.11th Banff knowledge acquisition for knowledge-based systems workshop, Banff, SRDG Publica-tions, University of Calgary,1998:1-19.
[65]张曌,夏国平,李雪峰.设计知识服务系统的知识获取.辽宁工程技术大学学报,2006(6)：928-931.
[66]Hiroko F, Dick B. Sinnons. Knowledge Conceptualization tool [J].IEEE Transaction on Knowledge and Data Engineering.1997,9(2):209-219.
[67]钱学森,于景元,戴汝为.一个科学新领域—开放的复杂巨系统及其方法论.自然杂志,1990,13(1)：3-10.
[68]钱学森.创建系统学.太原：山西科学技术出版社,2001.
[69]刘怡君,唐锡晋等.对偶刻度法及其在群体研讨中应用.管理评论,2004(10)：38-42.
[70]程发新,施国洪.决策共识中群体对方案评价的意见分歧度识别方法.中国系统工程学会第十五届年会,2008：197-204.
[71]Fan Z P, Chen X. Consensus measures and adjusting inconsistency of liguistic preference relations in group decision making. Fuzzy Systems and Knowledge Discovery:Second International Conference, FSKD, Changsha, China,2005:130-139.
[72]顾基发,王浣尘,唐锡晋.综合集成方法体系与系统学研究.北京：科学出版社,2007.
[73]Gu J F. Song W Q, Zhu Z X. Meta-synthesis and expert mining. Systems,Man and Cybernetics, 2008.SMC 2008,IEEE International,2008:467-471.
[74]Gu J F. Song W Q,Zhu Z X, Gao R. Liu Y J. Expert mining and TCM knowledge,KSS2008, Guangzhou,December11-13,2008.
[75]Domingos P. Toward knowledge-rich data mining. Journal of Data Mining and Knowledge Discovery,2007,15(1):21-28.
[76]D'Hondt M.,D'Hondt T.Is Domain Knowledge an Aspect? ECOOP Workshops,1999.
[77]李卫.领域知识的获取.北京邮电大学.2008
[78]黄书德,游伯龙.决策的合理性和决策者的习惯领域分析.焦作矿业学院学报.1990,21(4)：49-56.
[79]黄书德.多准则决策中的习惯领域分析.焦作矿业学院学报,1987,8(1)：4-13.
[80]陈世福,陈兆乾.人工智能与知识工程.南京大学出版社,1997：70-71.
[81]张攀,王波,卿晓霞.专家系统中多种知识表示方法的集成应用.微型电脑应用,2004,20(6)：4-6.
[82]徐舒.产生式表示的改进.上海：复旦大学,2005.
[83]李洁,丁颖.语义网—语义网格和语义网络.计算机与现代化,2007(7)：39-41.
[84]陈世福,陈兆乾著.人工智能与知识工程.南京：南京大学出版社,1997.
[85]GRUBER T R. A translation approach to portable ontology specifications.Knowledge Acquisition,1993,5(2):199-220.
[86]艾丹祥.基于本体论的知识检索研究.武汉：武汉大学,2004.
[87]Perez A G, Benjamns V R. Overview of knowledge sharing and reuse components:ontologies and problem solving methods.In:Proceedings of the IJCA I'99 Workshop on Ontologies and Problem Solving Methods.Stockholm:CEUR,1999.
[88]顾芳.多学科领域本体设计方法的研究.北京：中国科学院计算技术研究所,2004.
[89]王大治.本体开发及应用技术研究.成都：中国科学院成都计算机应用研究所,2005.
[90]杨春燕,蔡文著.可拓工程.北京：科学出版社,2007.
[91]Esapss,Expert judgment requirements and methods for ESA space systems. Issue 1, Draft 1,November,1991.
[92]Wang G Y. Domain-Oriented Data-Driven Data Mining(3DM):Simulation of Human Knowledge Understanding, WImBI 2006, LNAI 4845,2007:278-290.
[93]Piatetsky S G, Frawley W J. Knowledge discovery in databases.AAAI/MIT Press,1991.
[94]朱正祥,顾基发.基于综合集成的领域驱动深层知识挖掘研.中国系统工程学会第十五届年会,2008：121-127.
[95]He Z,Xu X, Deng S.Data mining for actionable knowledge:A survey,Technical report, Harbin Institute of Technology China,2005.
[96]李兴森.智能知识管理模式.中国科学院研究生院,2008.
[97]陈文伟.挖掘变化知识的可拓数据挖掘研究.中国工程科学,2008(11)：70-73.
[98]蔡文,杨春燕,陈文伟,李兴森.可拓集与可拓数据挖掘.北京：科学出版社,2008.
[99]张文凌.领域知识参与数据挖掘预处理阶段的研究.北方工业大学,2008.
[100]Nunez H, Angulo C, Catala A. Rule based learning systems from SVMs. In:European Symposium on Artificial Neural Networks Proceedings,2002:107-112.
[101]Fung G, S,Sandilya R, Bharat R. Rule extraction from linear support vector machchines, In:Proceeding of KDD'05,2005:32-40.
[102]Zhang P, Zhu X Q, Li A H,Zhang L L,Shi Y. Mining Knowledge from Multiple Criteria Linear Programming Models. Cutting-Edge Research Topics on Multiple Criteria Decision Making. Communications in Computer and Information Science,Volume 35.ISBN 978-3-642-02297-5. Springer Berlin Heidelberg,2009:170-175.
[103]Gu J F, Tang X J, Niu W Y. Meta-synthesis system approach for solving social complex problems, Proceedings of the First World Congress of the International Federation for Systems Research, JAIST Press,Kobe,2005:4-11.
[104]Gu J F. Meta-synthesis and Knowledge Creation, The Sino-Japan Workshop on Meta-synthesis and Creativity Support System, July 11-13,2005,Beijing, China.
[105]张秋.实现图书馆知识管理的最佳方法-综合集成.现代情报,2002(7)：128-129.
[106]于景元,周晓纪.从定性到定量的综合集成方法的实现和应用.系统工程理论与实践,2002,22(10)：26-32.
[107]戴汝为,王珏,田捷.智能系统的综合集成.杭州：浙江科学技术出版社,1995.
[108]戴汝为.系统科学与思维科学交叉发展的硕果——大成智慧工程.系统工程理论与实践,2002,22(5)：8-11.
[109]于景元,涂元季.从定性到定量的综合集成方法——案例研究.系统工程理论与实践,2002,22(5)：1-7&42.
[110]王浣尘.难度自增殖系统及其方法论,上海交通大学学报,1992,26(5)：5-11.
[111]王浣尘.系统策划.系统工程理论方法应用,1994,3(2)：1-5.
[112]王浣尘.旋进原则方法论.软科学方法论研究,1995：49-53.
[113]王浣尘.一种系统方法论—旋进原则.系统工程.1994,65(5)：9-12
[114]Agrawal R, Imielinski T, Swami A. Database mining:A performance perspective.IEEE Transactions on Knowledge and Data Engineering,1993:914-925.
[115]唐锡晋,刘怡君.有关社会焦点问题的群体研讨实验——定性综合集成的一种实践.系统工程理论与实践,2007,27(3)：42-49
[116]Tang X J. Towards meta-synthetic support to unstructured problem solving. Internation Journal of Information Technology & Decision Making,2007,6(3):491-508
[117]于景元,周晓纪.从定性到定量的综合集成方法的实现和应用.系统工程理论与实践,2002,22(10)：26-32.
[118]Tang X J, Zhang N, Wang Z. Augmented Support for Knowledge Sharing by Academic Conferences-On-line Conferencing Ba. In proceedings of IEEE WiCOM'07,Shanghai, July 2007:6400-6403.
[119]David B.On Dialogue.Routledge,London,1996.
[120]David B.On Creativity. Roufledge.London,1998.
[121]Gu J F, Tang X J. Meta-synthesis approach to complex system modeling. European Journal of Operational Research,2005(166):597-614.
[122]Gu J F, Tang X J. How to synthesize experts opinions-Building consensus from different perspectives,. KSS'2004, JAIST,2004:291-295.
[123]Mase K, Sumi Y, Nishimoto K. Informal conversation environment for collaborative concept formation[A].In:Ishida T eds.Community Computing:Collaboration over Global Information Networks[M].New York:John Wiley & Sons, Inc,1998:165-205.
[124]刘怡君,唐锡晋.一种支持协作与知识创造的场.管理科学学报,2006,9(1)：79-85.
[125]Butler C T, Rothstein A. On Conflict and Consensus.Food Not Bomb Publishing,1998.
[126]邢留伟.K_means算法在客户细分中的应用研究.西南财经大学,2004.
[127]Borg l,Groenen P. Modern Multidimensional Scaling:Theory and Applications.New York: Springer.Verlag,1997.
[128]Kohonen T.The self—organizing map.Proceedings of the IEEE,1990,78(9):1464-1480.
[129]Tenenbaum J B,Vin de Silva,John C.A global geometric framework for nonlinear dimensionality reduction.Science,2000(290):2319-2323.
[130]唐锡晋,刘怡君.从群体支持系统到创造力支持系统.系统工程理论与实践,2006(5)：63-71
[131]Cheng Y, Church G M. Biclustering of expression data. In Proc.ISMB'00. AAAI Press, 2000:93-103.
[132]Getz G, Levine E, Domany E. Coupled two-way clustering analysis of gene microarray data. PNAS1,2000:12079-12084.
[133]Tanay A, R. Sharan, Kupiec M, Shamir R. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci U S A.,2004,101(9):2981-2986.
[134]Kluger Y, Barsi R, Cheng J T, Gerstein M. Spectral biclustering of microarray data:coclustering genes and conditions.Genome Res,2003,13(4):703-16.
[135]李宝红,孙振球.对应分析在探讨疾病季节分布中的应用.中国卫生统计,2007,24(6)：593-596.
[136]金菊良,吴开亚,郦建强.巢湖水质安全评价的对应分析和投影寻踪熵耦合方法.四川大学学报,2007,39(6)：7-13.
[137]Goh K I,Cusick M E, Valle D, Childs B, Vidal M, Barabasi A L The human disease network. PNAS,2007,104(21):8685-8690.
[138]Zhou T, Ren J, Medo M, et al.Bipartite network projection and personal recommendation. Physical Review E,2007:1-7.
[139]陈文琴,陆君安,梁佳.疾病基因网络的二分图投影分析.复杂系统与复杂性科学,2009,6(1)：13-19.
[140]Predictive Model Markup Langage.http://www. dmg. org,2008
[141]张晓军,孟详武.预测模型标志语言应用分析.计算机工程与应用,2003,39(10)：187-190.
[142]DMG HomePage, An Introduction to PMML3.0. http://www.dmg.org/pmmlspecs.v3/pmml.v3.0/html, 2004.
[2]Frawley W, Piatetsky-Shapiro G, Matheus C. Knowledge Discovery in Databases:An Overview. AI Magazine,1992(13):213-228.
[3]Cao L B, Zhang C Q. Domain-Driven Actionable Knowledge Discovery in the Real World. PAKDD 2006,LNAI3918,821-830.
[4]Cao L B, Rick Schurmann R, Zhang C. Q. Domain-Driven In-Depth Pattern Discovery:A Practical Methodology. Australian Data Mining Conf.,2005.
[5]Silberschatz A, Tuzhilin A. What makes patterns interesting in Knowledge Discovery System. IEEE Trans,Knowledge and Data Engineering, Dec.1996:970-974.
[6]Shi Y, Li XS.Knowledge Management Platforms and Intelligent Knowledge beyond Data Mining. Advances in Multiple Criteria Decision Making and Human Systems Management: Knowledge and Wisdom, Y. Shi et al.(Eds.),IOS Press,2007:272-288.
[7]Yoon S C,Lawrence J, He,Park E K, Sam M. Using Domain Knowledge in Knowledge Discovery, Proceedings of the eighth international conference on Information and knowledge managemen,1999:243-250.
[8]Theresa I J, Thomas J. N. A domain-driven approach to improving search effectivenessin traditional online catalogsTheresa. Information & Management,2002:559-570.
[9]Sergei K,David H L. Domain Driven Web Development With Webjinn. OOPSLA'03,2003: 53-63.
[10]Fan J H, Li D Y. An overview of Data Mining and Knowledge Discovery. Journal of Computer Science and Technology,1998.
[11]Cross Industry Standard Process for Data Mining.http://www.crisp-dm. org,1999
[12]Agrawal R, Imielinski T, Swami A. Database mining:A performance perspective.IEEE Transactions on Knowledge and Data Engineering,1993:914-925.
[13]Agrawal R, Srikant R.Fast algorithm for mining association Rules in large databases[A].The International Conference on Very Large Data Bases.1994:487-499.
[14]Han J,Pei J,Yin Y. Ming frequent patterns without candidate generation.In Proc.2000ACM-SIGMOD Int.Conf.Management of Data(SIGMOD'00).Dallas,TX,2000: 1-12.
[15]Quinlan J R.Induction of decision trees. Machine Leaming,1986(1):81-106.
[16]Schlimmer J C,Fisher D.A case study of incremental concept induction.In Proceedings of AAAI-86[C].1986.
[17]Quinlan J R.C4.5:Programs for machine learning[M].San Mateo,California:Morgan Kaufmann,1993.
[18]Shi Y. Peng y,Xu W, Tang X. Data mining Via multiple criteria linear programming: applications in credit card protfolio management.International Journal of Information Technology and Decision Making.2002:131-151.
[19]Mehta M,Agrawal R,Rissanen J.SLIQ:A fast scalable classifier for data mining.Lecture notes in computer sciProc of the 5th int conf on extending database Tech.Avignon, France,1996:18-33.
[20]Shafer J C,Agrawal R,Mehta M.SPRINT:A scalable parallel classifier for data mining.Proc of the 22nd Int conf on very large databases.Mumbai(Bombay),India, 1996.
[21]Gehrke J,Ramakrishnan R,Ganti V. Rainforest a framework for fast decision tree construction of large datasets[A].In VLDB.1998.
[22]Friedman N,Geiger D,Goldszmidt M.Bayesian network classitier.Machine Learning, 1997,29(1):131-163.
[23]Liu B,Hsu M a Integrating classification and association rule mining.Proc ofthe 4th int conf on knowledge discovery and Data Mining.NUSA:AAAI Press,1998:80-86.
[24]Kovalerchuk B, Evgen V,James F R, Consistent and complete data and expert mining in medicine.in Medical Data Mining and Knowledge Discovery, Berlin:springer, 2001:238-280.
[25]Cortes C, Vapnik V.:Support vector networks, Machine learning,1995,20:273.
[26]MacQueen J. Some Methods for Classification-and Analysis of Multivariate Observations.Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability,1967.
[27]Ng R,Han J. CLARANS:A method for clustering objects for spatial datamining.IEEE Trans on Knowl,DataEng,2002,14(5):1003-1016.
[28]Zhang Tian, Ramakrishnan R, Livny M. BIRCH:An efficient data clustering method for very large databases.ACM SIGMOD Record,1996,25(2):103-114.
[29]Guha S, Rastogi R, Shim K. Cure:An Efficient Clustering Algorithm for Large Database.Proc.of ACM-SIGMOND Int.Conf. Management on Data, Seattle, Washington,1998: 73-84.
[30]Wang W, Yang J, Muntz R. STING:A Statistical Information Grid Approach to Spatial Data Mining. Proc.of the 23th International Conference on Very Large Data Bases,1997.
[31]Agrawal R, Gehrke J, Gunopulcs D. Automatic Subspace Clustering of High Dimensional Data for Data Mining Application. Proc.of ACM SIGMOD Intconfon Management on Data, Seattle,WA,1998:94-205.
[32]Hernandez S S. The merge purge problem for large database. Proceedings of the 1995 ACM SIGMOD international Conference on Management of Data, New York.USA:ACM press,1995:127-138.
[33]Gediminas A, Alexander T. Expert-Driven Validation of Rule-Based User Models in Personalization Applications.Data Mining and Knowledge Discovery,2001(5):33-58.
[34]Mihael A, Human Involvement and Interactivity of the Next Generation',s Data Mining Tools,CM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Santa Barbara, CA,2001.
[35]Siberschatz A, Tuzhilin A. What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Transactions on Knowledge and Data Engineering, Vol.8, No.6,1996: 970-974.
[36]Harleen. K, Actionable Rules:Issues and New Directions, Proceedings of world academy of science, Engineering and Technology,2005(5):1307-6884.
[37]Dragan G, Nada L. Generating Actionable Knowledge by Expert-Guided Subgroup Discovery,PKDD,2002:163-175.
[38]Gediminas A, Alexander T. Discovery of Actionable Patterns in Databases:The Action Hierarchy Approach. KDD'97,1997.
[39]Seunghyun I,Zbigniew W. Action Rule Extraction from a Decision Table: ARED.Springer-Verlag Berlin Heidelberg, LNAI4994,2008:160-168.
[40]Klaus J,Marc D. Mining Intrusion Detection Alarms for Actionable Knowledge.SIGKDD '02,2002:366-375.
[41]张玲玲,李军,石勇.基于数据挖掘的智能知识管理研究.中国管理科学,2009(15)：6-13.
[42]Song T K, Andrew L, Liz S.Domain Ontology Driven Data Mining,2007 ACM SIGKDD Workshop on Domain Driven Data Mining,2007:11-17.
[43]Song H,MA F y,LIU X Q. Ontology-based Knowledge Extraction from Hidden Web.Journal of Donghua University.2004(5):73-78.
[44]Pan D,SHEN D Y,ZHOU M X. Incorporating Domain Knowledge into Data Mining Process: An Ontology Based Framework. Wuhan University journal of Natural Science,2006 (1):165-169.
[45]Li Y H, Lu Z D, Sun X L, elt. Data Mining Ontology Development for High User Usability.Wuhan University Journal of Natural Sciences.2006(1):51-56.
[46]易国洪,章瑾.基于本体的数据挖掘方法研究.计算机与数字工程,2007(7)：42-44.
[47]Anthony K. H. Tungl, Jiawei Han,elt Constraint-Based Clustering in Large Databases.Springer-Verlag Berlin Heidelberg,ICDT 2001:405-419.
[48]Han J, Lakshmanan L V. S,Ng R T. Constraint-Based, Multidimensional Data Mining, Computer(special issues on Data Mining),1999,32(8):46-50.
[49]Juan M D,Paloma D, el at.A multi-Agent Approach to Colaborative Knowledge Production. Intelligent Agents for Data Mining and Information Retrieval,Idea Group Publishing 2004:42-53.
[50]Samhaa R E,Ahmed R,Yasser A. Using Dynamically Acquired Background Knowledge for Information extraction and Intelligent Search. Idea Group Publishing 2004:174-183.
[51]Andreevskaia A.Abi-Aad R. Radhakrishnan T. Agent-Mediated Knowledge Acquisition for User Profiling. Idea Group Publishing 2004:148-157.
[52]Zhu Z X, Song W Q, Gu J F. A Multi-agent and Data Mining Model for TCM Cases Knowledge Discovery,CCCM 2008:341-346.
[53]Zhang C Q, Zhang S C, In-Depth Data Mining and Its Application in Stock Market,Advanced Data Mining and Applications,First International Conference,ADMA 2005,Wuhan, China, July 22-24,2005,Proceedings,2005:13.
[54]Cao L.,Zhang, C.,Yu,P.,et al.Domain-Driven actionable knowledge discovery, IEEE Intelligent Systems,2007,22(4):78-89.
[55]Cao L B. Domain Driven Data Mining:Challenges and Prospects, IEEE Trans.on Knowledge and Data Engineering,2009.
[56]Cao, L.,Zhang, C.:Knowledge Actionability:Satisfying Technical and Business Interestingness,International Journal of Business Intelligence and Data Mining, 2007,2(4):496-514.
[57]Cao, L.,Zhang, C.:The Evolution of KDD:Towards Domain-Driven Data Mining, International Journal of Pattern Recognition and Artificial Intelligence, 2007,21(4):677-692,.
[58]顾基发.意见综合—怎样达成共识.系统工程学报,2001(5)：340-348.
[59]顾基发,王浣尘,唐锡晋.综合集成与复杂系统专辑序.系统工程学报,2001,16(5)：321.
[60]顾基发,唐锡晋.综合集成与知识科学.系统工程理论与实践,2002(10)：2-7.
[61]Kovalerchuk B,Delizy F. Visual data mining using monotone Boolean functions.In:B. Kovalerchuk and J. Schwing, Editors,Visual and spatial analysis,Springer, Dordrecht 2004.
[62]Kovalerchuk B, Vityaev, J. Ruiz.Consistent knowledge discovery in medical diagnosis. IEEE Engineering in Medicine and Biology, (Special issue on Data Mining and Knowledge Discovery),2000:26-37.
[63]Compton P, Horn R, Quinlan R, et al.Maintaining an Expert System in J R Quinlan, Proc. of the 4th Conf. on Application of Expert System.1988.
[64]Kang B, Compton P, et al.Simulated Expert Evaluation of Multiple Classification Ripple Down Rules.11th Banff knowledge acquisition for knowledge-based systems workshop, Banff, SRDG Publica-tions, University of Calgary,1998:1-19.
[65]张曌,夏国平,李雪峰.设计知识服务系统的知识获取.辽宁工程技术大学学报,2006(6)：928-931.
[66]Hiroko F, Dick B. Sinnons. Knowledge Conceptualization tool [J].IEEE Transaction on Knowledge and Data Engineering.1997,9(2):209-219.
[67]钱学森,于景元,戴汝为.一个科学新领域—开放的复杂巨系统及其方法论.自然杂志,1990,13(1)：3-10.
[68]钱学森.创建系统学.太原：山西科学技术出版社,2001.
[69]刘怡君,唐锡晋等.对偶刻度法及其在群体研讨中应用.管理评论,2004(10)：38-42.
[70]程发新,施国洪.决策共识中群体对方案评价的意见分歧度识别方法.中国系统工程学会第十五届年会,2008：197-204.
[71]Fan Z P, Chen X. Consensus measures and adjusting inconsistency of liguistic preference relations in group decision making. Fuzzy Systems and Knowledge Discovery:Second International Conference, FSKD, Changsha, China,2005:130-139.
[72]顾基发,王浣尘,唐锡晋.综合集成方法体系与系统学研究.北京：科学出版社,2007.
[73]Gu J F. Song W Q, Zhu Z X. Meta-synthesis and expert mining. Systems,Man and Cybernetics, 2008.SMC 2008,IEEE International,2008:467-471.
[74]Gu J F. Song W Q,Zhu Z X, Gao R. Liu Y J. Expert mining and TCM knowledge,KSS2008, Guangzhou,December11-13,2008.
[75]Domingos P. Toward knowledge-rich data mining. Journal of Data Mining and Knowledge Discovery,2007,15(1):21-28.
[76]D'Hondt M.,D'Hondt T.Is Domain Knowledge an Aspect? ECOOP Workshops,1999.
[77]李卫.领域知识的获取.北京邮电大学.2008
[78]黄书德,游伯龙.决策的合理性和决策者的习惯领域分析.焦作矿业学院学报.1990,21(4)：49-56.
[79]黄书德.多准则决策中的习惯领域分析.焦作矿业学院学报,1987,8(1)：4-13.
[80]陈世福,陈兆乾.人工智能与知识工程.南京大学出版社,1997：70-71.
[81]张攀,王波,卿晓霞.专家系统中多种知识表示方法的集成应用.微型电脑应用,2004,20(6)：4-6.
[82]徐舒.产生式表示的改进.上海：复旦大学,2005.
[83]李洁,丁颖.语义网—语义网格和语义网络.计算机与现代化,2007(7)：39-41.
[84]陈世福,陈兆乾著.人工智能与知识工程.南京：南京大学出版社,1997.
[85]GRUBER T R. A translation approach to portable ontology specifications.Knowledge Acquisition,1993,5(2):199-220.
[86]艾丹祥.基于本体论的知识检索研究.武汉：武汉大学,2004.
[87]Perez A G, Benjamns V R. Overview of knowledge sharing and reuse components:ontologies and problem solving methods.In:Proceedings of the IJCA I'99 Workshop on Ontologies and Problem Solving Methods.Stockholm:CEUR,1999.
[88]顾芳.多学科领域本体设计方法的研究.北京：中国科学院计算技术研究所,2004.
[89]王大治.本体开发及应用技术研究.成都：中国科学院成都计算机应用研究所,2005.
[90]杨春燕,蔡文著.可拓工程.北京：科学出版社,2007.
[91]Esapss,Expert judgment requirements and methods for ESA space systems. Issue 1, Draft 1,November,1991.
[92]Wang G Y. Domain-Oriented Data-Driven Data Mining(3DM):Simulation of Human Knowledge Understanding, WImBI 2006, LNAI 4845,2007:278-290.
[93]Piatetsky S G, Frawley W J. Knowledge discovery in databases.AAAI/MIT Press,1991.
[94]朱正祥,顾基发.基于综合集成的领域驱动深层知识挖掘研.中国系统工程学会第十五届年会,2008：121-127.
[95]He Z,Xu X, Deng S.Data mining for actionable knowledge:A survey,Technical report, Harbin Institute of Technology China,2005.
[96]李兴森.智能知识管理模式.中国科学院研究生院,2008.
[97]陈文伟.挖掘变化知识的可拓数据挖掘研究.中国工程科学,2008(11)：70-73.
[98]蔡文,杨春燕,陈文伟,李兴森.可拓集与可拓数据挖掘.北京：科学出版社,2008.
[99]张文凌.领域知识参与数据挖掘预处理阶段的研究.北方工业大学,2008.
[100]Nunez H, Angulo C, Catala A. Rule based learning systems from SVMs. In:European Symposium on Artificial Neural Networks Proceedings,2002:107-112.
[101]Fung G, S,Sandilya R, Bharat R. Rule extraction from linear support vector machchines, In:Proceeding of KDD'05,2005:32-40.
[102]Zhang P, Zhu X Q, Li A H,Zhang L L,Shi Y. Mining Knowledge from Multiple Criteria Linear Programming Models. Cutting-Edge Research Topics on Multiple Criteria Decision Making. Communications in Computer and Information Science,Volume 35.ISBN 978-3-642-02297-5. Springer Berlin Heidelberg,2009:170-175.
[103]Gu J F, Tang X J, Niu W Y. Meta-synthesis system approach for solving social complex problems, Proceedings of the First World Congress of the International Federation for Systems Research, JAIST Press,Kobe,2005:4-11.
[104]Gu J F. Meta-synthesis and Knowledge Creation, The Sino-Japan Workshop on Meta-synthesis and Creativity Support System, July 11-13,2005,Beijing, China.
[105]张秋.实现图书馆知识管理的最佳方法-综合集成.现代情报,2002(7)：128-129.
[106]于景元,周晓纪.从定性到定量的综合集成方法的实现和应用.系统工程理论与实践,2002,22(10)：26-32.
[107]戴汝为,王珏,田捷.智能系统的综合集成.杭州：浙江科学技术出版社,1995.
[108]戴汝为.系统科学与思维科学交叉发展的硕果——大成智慧工程.系统工程理论与实践,2002,22(5)：8-11.
[109]于景元,涂元季.从定性到定量的综合集成方法——案例研究.系统工程理论与实践,2002,22(5)：1-7&42.
[110]王浣尘.难度自增殖系统及其方法论,上海交通大学学报,1992,26(5)：5-11.
[111]王浣尘.系统策划.系统工程理论方法应用,1994,3(2)：1-5.
[112]王浣尘.旋进原则方法论.软科学方法论研究,1995：49-53.
[113]王浣尘.一种系统方法论—旋进原则.系统工程.1994,65(5)：9-12
[114]Agrawal R, Imielinski T, Swami A. Database mining:A performance perspective.IEEE Transactions on Knowledge and Data Engineering,1993:914-925.
[115]唐锡晋,刘怡君.有关社会焦点问题的群体研讨实验——定性综合集成的一种实践.系统工程理论与实践,2007,27(3)：42-49
[116]Tang X J. Towards meta-synthetic support to unstructured problem solving. Internation Journal of Information Technology & Decision Making,2007,6(3):491-508
[117]于景元,周晓纪.从定性到定量的综合集成方法的实现和应用.系统工程理论与实践,2002,22(10)：26-32.
[118]Tang X J, Zhang N, Wang Z. Augmented Support for Knowledge Sharing by Academic Conferences-On-line Conferencing Ba. In proceedings of IEEE WiCOM'07,Shanghai, July 2007:6400-6403.
[119]David B.On Dialogue.Routledge,London,1996.
[120]David B.On Creativity. Roufledge.London,1998.
[121]Gu J F, Tang X J. Meta-synthesis approach to complex system modeling. European Journal of Operational Research,2005(166):597-614.
[122]Gu J F, Tang X J. How to synthesize experts opinions-Building consensus from different perspectives,. KSS'2004, JAIST,2004:291-295.
[123]Mase K, Sumi Y, Nishimoto K. Informal conversation environment for collaborative concept formation[A].In:Ishida T eds.Community Computing:Collaboration over Global Information Networks[M].New York:John Wiley & Sons, Inc,1998:165-205.
[124]刘怡君,唐锡晋.一种支持协作与知识创造的场.管理科学学报,2006,9(1)：79-85.
[125]Butler C T, Rothstein A. On Conflict and Consensus.Food Not Bomb Publishing,1998.
[126]邢留伟.K_means算法在客户细分中的应用研究.西南财经大学,2004.
[127]Borg l,Groenen P. Modern Multidimensional Scaling:Theory and Applications.New York: Springer.Verlag,1997.
[128]Kohonen T.The self—organizing map.Proceedings of the IEEE,1990,78(9):1464-1480.
[129]Tenenbaum J B,Vin de Silva,John C.A global geometric framework for nonlinear dimensionality reduction.Science,2000(290):2319-2323.
[130]唐锡晋,刘怡君.从群体支持系统到创造力支持系统.系统工程理论与实践,2006(5)：63-71
[131]Cheng Y, Church G M. Biclustering of expression data. In Proc.ISMB'00. AAAI Press, 2000:93-103.
[132]Getz G, Levine E, Domany E. Coupled two-way clustering analysis of gene microarray data. PNAS1,2000:12079-12084.
[133]Tanay A, R. Sharan, Kupiec M, Shamir R. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci U S A.,2004,101(9):2981-2986.
[134]Kluger Y, Barsi R, Cheng J T, Gerstein M. Spectral biclustering of microarray data:coclustering genes and conditions.Genome Res,2003,13(4):703-16.
[135]李宝红,孙振球.对应分析在探讨疾病季节分布中的应用.中国卫生统计,2007,24(6)：593-596.
[136]金菊良,吴开亚,郦建强.巢湖水质安全评价的对应分析和投影寻踪熵耦合方法.四川大学学报,2007,39(6)：7-13.
[137]Goh K I,Cusick M E, Valle D, Childs B, Vidal M, Barabasi A L The human disease network. PNAS,2007,104(21):8685-8690.
[138]Zhou T, Ren J, Medo M, et al.Bipartite network projection and personal recommendation. Physical Review E,2007:1-7.
[139]陈文琴,陆君安,梁佳.疾病基因网络的二分图投影分析.复杂系统与复杂性科学,2009,6(1)：13-19.
[140]Predictive Model Markup Langage.http://www. dmg. org,2008
[141]张晓军,孟详武.预测模型标志语言应用分析.计算机工程与应用,2003,39(10)：187-190.
[142]DMG HomePage, An Introduction to PMML3.0. http://www.dmg.org/pmmlspecs.v3/pmml.v3.0/html, 2004.