供水公司CRM系统的构建与数据挖掘的运用
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摘要
企业管理从过去的“产品”导向转变为“客户”导向，客户关系管理(CRM)逐渐成为企业的焦点，也成为计算机领域的一个活跃的研究领域。CRM通过提供更快速和周到的优质服务提高客户的忠诚度和满意度；通过对业务流程的全面管理降低企业的成本。供水公司作为公用事业单位，为了提高部门的工作效率、节约开支、提高客户服务质量，并由此带来新的发展，也必须建立一个以方便客户为目的管理系统。
本文首先介绍了CRM的管理内涵与管理思想，CRM的体系结构及CRM的核心技术，接着从硬件模式、网络解决方案、软件体系三个方面构建了四川省XX市供水总公司的CRM系统。
数据挖掘作为CRM的核心技术之一，通过对大量的数据与信息进行快速有效地分析和处理，找出规律和模式，获取知识帮助企业进行决策。关联规则的发现是数据挖掘技术的一种简单又很实用的方法。本文最后研究了关联规则的数据挖掘技术在供水公司CRM系统中的运用，并对其算法核心予以实现，为整个CRM系统开发提供咨询参考。
Enterprise Management orientation transform from "product" to "customer". Customer Relationship Management (CRM) becomes the focus of enterprise and an active research field of computer science. CRM could communicate effectively with customers and satisfy their different requirement. On the other hand; CRM manages the workflows of enterprise and reduces their cost. In order to develop better, Water supplying enterprise, the unit for public use, must improve productiveness, cut down expense, give more service to customers, and first of all, must build a CRM system.
First, the connotation, the management theory, the components and the main technology of CRM are studied in this paper. Then, the system of water supplying enterprise was created based on hardware, software and network.
Data mining plays an important role in the CRM. System. CRM finds some rules and patterns through dealing with mass data and information effectively, which supports enterprise decision. Mining for association rules is a simple and very useful data mining method. Last, the paper applied association rules of data mining to CRM system of water supplying enterprise and realized the algorithm of association rules, which offers reference for the CRM system development.
本文首先介绍了CRM的管理内涵与管理思想，CRM的体系结构及CRM的核心技术，接着从硬件模式、网络解决方案、软件体系三个方面构建了四川省XX市供水总公司的CRM系统。
数据挖掘作为CRM的核心技术之一，通过对大量的数据与信息进行快速有效地分析和处理，找出规律和模式，获取知识帮助企业进行决策。关联规则的发现是数据挖掘技术的一种简单又很实用的方法。本文最后研究了关联规则的数据挖掘技术在供水公司CRM系统中的运用，并对其算法核心予以实现，为整个CRM系统开发提供咨询参考。
Enterprise Management orientation transform from "product" to "customer". Customer Relationship Management (CRM) becomes the focus of enterprise and an active research field of computer science. CRM could communicate effectively with customers and satisfy their different requirement. On the other hand; CRM manages the workflows of enterprise and reduces their cost. In order to develop better, Water supplying enterprise, the unit for public use, must improve productiveness, cut down expense, give more service to customers, and first of all, must build a CRM system.
First, the connotation, the management theory, the components and the main technology of CRM are studied in this paper. Then, the system of water supplying enterprise was created based on hardware, software and network.
Data mining plays an important role in the CRM. System. CRM finds some rules and patterns through dealing with mass data and information effectively, which supports enterprise decision. Mining for association rules is a simple and very useful data mining method. Last, the paper applied association rules of data mining to CRM system of water supplying enterprise and realized the algorithm of association rules, which offers reference for the CRM system development.
引文
1．许芳，现代企业管理中的CRM，决策借鉴，2002．15(1)：40～41
2．陈京民，祁沁午，朱惠云，客户关系管理(CRM)在商业企业中的运用，企业管理，2002．4，34～36
3．陈旭，CRM综述．计算机应用研究，2001．(8)；8～10
4．孟凡强，解读CRM[J]，计算机世界，2000，(46)：6～7
5．张晓航，吕廷杰，客户关系管理系统，电信技术，2002．8：6～9
6．贺奇，郑岩，魏藜等译，构建面向CRM的数据挖掘应用，人民邮电出版社，2001．8
7．史忠植著，知识发现，清华大学出版社，2002．1
8. W.H.Inmon. Building the Data Warehouse, Second Edition.2000.5
9．彭小根编著，雨人科技策划，数据仓库技术与实现，电子工业出版社，2002．6
10. U.M.Fayyad, G.Piatetsdy-Shapiro, P.Smyth, and R.Uthurusamy, editors. Advances in Knowledge Didcovery and Data Mining. Cambridge, MA:AAAⅠ/MIT Press, 1996.
11．林杰，薜华成，数据挖掘管理系统，微型电脑应用，2000．16(11)：13～15
12. A.Berson,S.J.Smith, and K.Thearling. Building Data Mining Applications for CRM. New York: McGraw-Hill,1999.
13．陈卫华，朱仲英，数据挖掘在CRM中的应用，微电脑应用，2001．17(10)：26～28
14．温凤兰，CRM与数据挖掘技术，电脑应用，2002．7：12～14
15．陈文伟，决策支持系统及开发(第二版)，清华大学出版社，2000．2
16．张春阳，周继恩，刘贵全等，基于数据仓库的决策支持系统的构建，计算机工程，2002．28(4)：249～241．
17. Chen Q, Hsu M, Dayal U. A Data-warehouse/OLAP Framework for Scalable Telecommunication Tandem Traffic Analysis. In 16th International Conference on Data Engineering, Proceedings.2000:201～210
18．尹春华，顾培亮，决策支持系统研究现状及发展趋势，决策借鉴，2002．15(2)：41～45
19．熊桂喜，王小虎译，计算机网络(第三版)，清华大学出版社，2000．10
20. R.Agrawal, T.Imielinski, and A.Swami. Database mining; A performance perspective. IEEE Trans. Knowledge and Data Engineering,5:914～925,1993.
21. R.Agrawal, T.Imielinski, and A.Swami. Mining association rules between sets of items in large databases. In Proc, 1993 ACM-SIGMOD Int. Conf. Management of Data(SIGMOD'93), pages 207～216, Washington, DC, May 1993.
22. R.Srikant, R.Agrawal, Mining sequential patterns: generalizations and performance Improvements[A]. Proc International Conference on Extending Database Technology[C].
Avignon France, 1996,3～17.
23．陈金玉，樊兴华，曹长修，序列模式的一种挖掘算法，重庆大学学报，2001．24(1)：92～94．
24．周斌，吴泉源，序列模式挖掘的一种渐进算法，计算机学报，1999．22(8)：882～887
25. S.M.Weiss and C.A.Kulikowski, Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems. San Mateo, CA: Morgan Kaufmann, 1991.
26．朱天翔，李力，许占文，分类特征规则的数据挖掘技术，沈阳工业大学学报(自然科学版)，1999．12：15～18
27. A.K.Jain, M.N Murty, and P.J.Flynn. Data clustering: A Survey. ACM Comput.Surv. 31:264～323,1999.
28．高新波，谢维信，模糊聚类理论发展及应用的研究进展，科学通报，1999．44(21)：2242～2250．
29．Jiawei Han，Micheline Kamber，范明，孟小峰等译，数据挖掘—概念与技术[M]，机械工业出版社，2001，8，149～151
30. M.Houtsma, A Swami. Set-oriented mining of association rules[R]. Research Report RJ9567,IBM Almaden Research Center, 1993.10
31. R.Agrawal and R.Srikant, Fast algorithms for mining association rules in large databases. In Research Report RJ9839,IBM AlMaden Research Center, San Jose, CA, June 1994
32. H.Mannila, H.Toivonen, and A.I.Verkamo.Efficient algorithms for discovering association rules.In Proc.AAAI'94 Workshop Knowledge Discovery in Databases(KDD'94),pages 181～192,Seattle,WA, July 1994.
33. A.Savasere, E.Omiecinski, and S.Navathe. An efficient algorithm for mining association rules in large databases. In Proc.1995 Int. Conf. Very Large Data Bases(VLDB'95), pages 432～443, Zurich, Switzerland, Sept. 1995.
34. J.Spark, M.S. Chen, and P.S.Yu..Efficient parattel mining for association rules. In Proc.4th Int. Conf. Information and Knowledge Management, Pages 31～36, Baltimore, MD,Nov.1995.
35. H.Thomsen. Sampling large database for association rules. In Proc.1996 Int. Conf. Very large Data Bases(VLDB'96), pages 134～145, Bombay, India, Sept. 1996
36．周欣，沙朝锋，朱杨勇，兴趣度—关联规则的又一个阈值，计算机研究与发展，2000．5，37(5)，627～633
37．刘玉葆，孟志青，交易数据库中带有空间性约束的关联规则的采掘，计算机工程与应用，2000．9：110～111．
38．高飞，谢维信，多概念层次的数值关联规则挖掘，计算机科学，2001．28(6)：85～87
2．陈京民，祁沁午，朱惠云，客户关系管理(CRM)在商业企业中的运用，企业管理，2002．4，34～36
3．陈旭，CRM综述．计算机应用研究，2001．(8)；8～10
4．孟凡强，解读CRM[J]，计算机世界，2000，(46)：6～7
5．张晓航，吕廷杰，客户关系管理系统，电信技术，2002．8：6～9
6．贺奇，郑岩，魏藜等译，构建面向CRM的数据挖掘应用，人民邮电出版社，2001．8
7．史忠植著，知识发现，清华大学出版社，2002．1
8. W.H.Inmon. Building the Data Warehouse, Second Edition.2000.5
9．彭小根编著，雨人科技策划，数据仓库技术与实现，电子工业出版社，2002．6
10. U.M.Fayyad, G.Piatetsdy-Shapiro, P.Smyth, and R.Uthurusamy, editors. Advances in Knowledge Didcovery and Data Mining. Cambridge, MA:AAAⅠ/MIT Press, 1996.
11．林杰，薜华成，数据挖掘管理系统，微型电脑应用，2000．16(11)：13～15
12. A.Berson,S.J.Smith, and K.Thearling. Building Data Mining Applications for CRM. New York: McGraw-Hill,1999.
13．陈卫华，朱仲英，数据挖掘在CRM中的应用，微电脑应用，2001．17(10)：26～28
14．温凤兰，CRM与数据挖掘技术，电脑应用，2002．7：12～14
15．陈文伟，决策支持系统及开发(第二版)，清华大学出版社，2000．2
16．张春阳，周继恩，刘贵全等，基于数据仓库的决策支持系统的构建，计算机工程，2002．28(4)：249～241．
17. Chen Q, Hsu M, Dayal U. A Data-warehouse/OLAP Framework for Scalable Telecommunication Tandem Traffic Analysis. In 16th International Conference on Data Engineering, Proceedings.2000:201～210
18．尹春华，顾培亮，决策支持系统研究现状及发展趋势，决策借鉴，2002．15(2)：41～45
19．熊桂喜，王小虎译，计算机网络(第三版)，清华大学出版社，2000．10
20. R.Agrawal, T.Imielinski, and A.Swami. Database mining; A performance perspective. IEEE Trans. Knowledge and Data Engineering,5:914～925,1993.
21. R.Agrawal, T.Imielinski, and A.Swami. Mining association rules between sets of items in large databases. In Proc, 1993 ACM-SIGMOD Int. Conf. Management of Data(SIGMOD'93), pages 207～216, Washington, DC, May 1993.
22. R.Srikant, R.Agrawal, Mining sequential patterns: generalizations and performance Improvements[A]. Proc International Conference on Extending Database Technology[C].
Avignon France, 1996,3～17.
23．陈金玉，樊兴华，曹长修，序列模式的一种挖掘算法，重庆大学学报，2001．24(1)：92～94．
24．周斌，吴泉源，序列模式挖掘的一种渐进算法，计算机学报，1999．22(8)：882～887
25. S.M.Weiss and C.A.Kulikowski, Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems. San Mateo, CA: Morgan Kaufmann, 1991.
26．朱天翔，李力，许占文，分类特征规则的数据挖掘技术，沈阳工业大学学报(自然科学版)，1999．12：15～18
27. A.K.Jain, M.N Murty, and P.J.Flynn. Data clustering: A Survey. ACM Comput.Surv. 31:264～323,1999.
28．高新波，谢维信，模糊聚类理论发展及应用的研究进展，科学通报，1999．44(21)：2242～2250．
29．Jiawei Han，Micheline Kamber，范明，孟小峰等译，数据挖掘—概念与技术[M]，机械工业出版社，2001，8，149～151
30. M.Houtsma, A Swami. Set-oriented mining of association rules[R]. Research Report RJ9567,IBM Almaden Research Center, 1993.10
31. R.Agrawal and R.Srikant, Fast algorithms for mining association rules in large databases. In Research Report RJ9839,IBM AlMaden Research Center, San Jose, CA, June 1994
32. H.Mannila, H.Toivonen, and A.I.Verkamo.Efficient algorithms for discovering association rules.In Proc.AAAI'94 Workshop Knowledge Discovery in Databases(KDD'94),pages 181～192,Seattle,WA, July 1994.
33. A.Savasere, E.Omiecinski, and S.Navathe. An efficient algorithm for mining association rules in large databases. In Proc.1995 Int. Conf. Very Large Data Bases(VLDB'95), pages 432～443, Zurich, Switzerland, Sept. 1995.
34. J.Spark, M.S. Chen, and P.S.Yu..Efficient parattel mining for association rules. In Proc.4th Int. Conf. Information and Knowledge Management, Pages 31～36, Baltimore, MD,Nov.1995.
35. H.Thomsen. Sampling large database for association rules. In Proc.1996 Int. Conf. Very large Data Bases(VLDB'96), pages 134～145, Bombay, India, Sept. 1996
36．周欣，沙朝锋，朱杨勇，兴趣度—关联规则的又一个阈值，计算机研究与发展，2000．5，37(5)，627～633
37．刘玉葆，孟志青，交易数据库中带有空间性约束的关联规则的采掘，计算机工程与应用，2000．9：110～111．
38．高飞，谢维信，多概念层次的数值关联规则挖掘，计算机科学，2001．28(6)：85～87