基于数据挖掘技术的个人客户识别模型的研究及应用
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摘要
随着电信企业的重组和3G牌照的发放,中国电信市场进入更加激烈的全业务竞争时代,如何适应日趋激烈的市场竞争环境,成为电信企业的重要问题。电信行业重组,中国移动面临着诸多的压力:如何应对由技术发展日新月异而引起的错综复杂的产业格局?如何经营自主研发的、不太成熟、在国际舞台上支撑力度弱的TD标准?如何应对“一家独大”局面受到的不对称监管的困境?如何经营已经开始的全业务模式?
如果还是通过一些传统的、简单的数据统计,对于数据的利用仅限于数据的表层信息,而没有去挖掘数据之间更加深层次的信息,是不可能从如此海量的数据和信息中找到解决复杂问题的规律的。数据挖掘技术是一种功能强大的新技术,它能帮助企业在构建数据仓库中找到最重要的信息。本文利用数据挖掘技术找到个人客户的流动特征和规律,并应用数据挖掘
技术来构建个人客户识别模型,主要研究内容有:分析并研究数据挖掘技术在移动个人客户识别模型中的应用;研究并初步实现了个人客户识别模型在移动业务相关领域的应用;针对C4.5可以通过改变样本的权重来处理属性值的缺失,利用C4.5的这种特性,对C4.5算法稍做改进可以得到一个基于代价敏感的变种算法C4.5_cs,并将这种算法应用到个人客户识别模型中;在建立个人客户认别模型时,提出了交往指数和符合率的指标,将这两个指标应用到模型构建中,对应用前后的C4.5_cs算法做了对比分析。
本文主要基于中国移动现有的经营分析系统,针对中国移动的现实需求,重点研究讨论了决策树算法,从海量的业务系统数据中,分析挖掘个人客户的流动特征,利用决策树算法C4.5_cs建立个人客户识别模型,并利用模型生成的规则实现了模型的应用;模型通过对用户通话特征及个人信息特征等多种信息的分析挖掘,以个人客户交往圈匹配算法为核心,在用户全生命周期(获取期、成长期、成熟期、衰退期、流失期)之外,找到用户在获取期之前(游离期)的来源以及在流失期之后(离网期)的不同去向,使我们对用户的了解更加深入,从而为业务人员进一步了解客户提供帮助,为进行挽留用户和精准营销提供支持。
With the telecom restructuring and the issuance of 3G licenses, China's telecommunications market into the more intense competition in all business era. how to adapt to the increasingly fierce market competition has become the important issue in terms of the telecom business.
Telecom industry restructuring, China Mobile is facing many pressures: how to respond to rapid technological development caused by the intricacies of the industry structure? How to run a self-developed, less mature, in the international arena,weak support for the TD standard? How to deal with "a dominant" situation by the plight of the asymmetric regulation? How to run the forthcoming full-service model?
Traditional, simple statistics, use of data only for the surface data information between the data and not to dig more in-depth information, is not possible from such a mass of data and information to find solutions to complex problems in the law .
By using data mining techniques to find the flow characteristics of individual clients and the law, and apply data mining techniques to build individual customer identification model, the main contents are: analysis and research of data mining technology in the mobile personal customer identification model in the application; study and preliminary recognition model to achieve the individual customers in the mobile business-related field of application; for C4.5 by changing the weight of the sample to deal with missing attribute values, using C4.5 of this characteristic, slight improvement on the C4.5 algorithm can be A variant algorithm based on cost-sensitive C4.5_cs, and this algorithm is applied to individual clients to identify the model; in the establishment of individual customers do not recognize model proposed index and found the rate of exchange index is applied to these two indicators Model, the before and after its application to do a comparative analysis C4.5_cs algorithm.
This article is mainly based on China Mobile's existing management analysis system for the practical needs of China Mobile, the focus of the discussion of the decision tree algorithm, the business system from the mass of data, analysis of the flow characteristics of individual customers mining using decision tree algorithm C4.5_cscreate a personal customer identification model, and the rules generated using the model to achieve application of the model; model calls on the user characteristics and features of personal information mining and other analytical information to individual customers as the core communication loop matching algorithm, the user's full of lifecycle (access, growth, maturity, decline, loss of period), the period before to find the user access (free) the source and the drain after a period of (off-grid period) of the different fate, so that our users more in-depth understanding, so as to further understanding of the business staff provide help to retain customers and to provide support for precision marketing.
如果还是通过一些传统的、简单的数据统计,对于数据的利用仅限于数据的表层信息,而没有去挖掘数据之间更加深层次的信息,是不可能从如此海量的数据和信息中找到解决复杂问题的规律的。数据挖掘技术是一种功能强大的新技术,它能帮助企业在构建数据仓库中找到最重要的信息。本文利用数据挖掘技术找到个人客户的流动特征和规律,并应用数据挖掘
技术来构建个人客户识别模型,主要研究内容有:分析并研究数据挖掘技术在移动个人客户识别模型中的应用;研究并初步实现了个人客户识别模型在移动业务相关领域的应用;针对C4.5可以通过改变样本的权重来处理属性值的缺失,利用C4.5的这种特性,对C4.5算法稍做改进可以得到一个基于代价敏感的变种算法C4.5_cs,并将这种算法应用到个人客户识别模型中;在建立个人客户认别模型时,提出了交往指数和符合率的指标,将这两个指标应用到模型构建中,对应用前后的C4.5_cs算法做了对比分析。
本文主要基于中国移动现有的经营分析系统,针对中国移动的现实需求,重点研究讨论了决策树算法,从海量的业务系统数据中,分析挖掘个人客户的流动特征,利用决策树算法C4.5_cs建立个人客户识别模型,并利用模型生成的规则实现了模型的应用;模型通过对用户通话特征及个人信息特征等多种信息的分析挖掘,以个人客户交往圈匹配算法为核心,在用户全生命周期(获取期、成长期、成熟期、衰退期、流失期)之外,找到用户在获取期之前(游离期)的来源以及在流失期之后(离网期)的不同去向,使我们对用户的了解更加深入,从而为业务人员进一步了解客户提供帮助,为进行挽留用户和精准营销提供支持。
With the telecom restructuring and the issuance of 3G licenses, China's telecommunications market into the more intense competition in all business era. how to adapt to the increasingly fierce market competition has become the important issue in terms of the telecom business.
Telecom industry restructuring, China Mobile is facing many pressures: how to respond to rapid technological development caused by the intricacies of the industry structure? How to run a self-developed, less mature, in the international arena,weak support for the TD standard? How to deal with "a dominant" situation by the plight of the asymmetric regulation? How to run the forthcoming full-service model?
Traditional, simple statistics, use of data only for the surface data information between the data and not to dig more in-depth information, is not possible from such a mass of data and information to find solutions to complex problems in the law .
By using data mining techniques to find the flow characteristics of individual clients and the law, and apply data mining techniques to build individual customer identification model, the main contents are: analysis and research of data mining technology in the mobile personal customer identification model in the application; study and preliminary recognition model to achieve the individual customers in the mobile business-related field of application; for C4.5 by changing the weight of the sample to deal with missing attribute values, using C4.5 of this characteristic, slight improvement on the C4.5 algorithm can be A variant algorithm based on cost-sensitive C4.5_cs, and this algorithm is applied to individual clients to identify the model; in the establishment of individual customers do not recognize model proposed index and found the rate of exchange index is applied to these two indicators Model, the before and after its application to do a comparative analysis C4.5_cs algorithm.
This article is mainly based on China Mobile's existing management analysis system for the practical needs of China Mobile, the focus of the discussion of the decision tree algorithm, the business system from the mass of data, analysis of the flow characteristics of individual customers mining using decision tree algorithm C4.5_cscreate a personal customer identification model, and the rules generated using the model to achieve application of the model; model calls on the user characteristics and features of personal information mining and other analytical information to individual customers as the core communication loop matching algorithm, the user's full of lifecycle (access, growth, maturity, decline, loss of period), the period before to find the user access (free) the source and the drain after a period of (off-grid period) of the different fate, so that our users more in-depth understanding, so as to further understanding of the business staff provide help to retain customers and to provide support for precision marketing.
引文
[1] Pang-Ning Tan,Michael Steinbach,Vipin Kumar著.数据挖掘导论[M].北京:人民邮电出版社,2006.
[2]W.H.Inmon:Building the Data Warehouse. Second Edition [M].1998.
[3]RUD O P.数据挖掘实践[M].北京:机械工业出版社,2003.
[4] AlexBerson,Stephensmith,KurtThearling著.构件面向CRM的数据挖掘应用[M],贺奇等译.北京.人民邮电出版社,2001.
[5]邵峰晶,于忠清.数据挖掘原理与算法[M].北京:中国水利水电出版社,2003.
[6]章兢,数据挖掘算法及其工程应用[M].北京.机械工业出版社,2006.
[7]胡可云,田凤占,黄厚宽.数据挖掘理论与应用[M].北京:清华大学出版社,2008.
[8]章兢,数据挖掘算法及其工程应用[M].北京.机械工业出版社,2008.
[9]周志华,王珏著.机器学习及其应用[M].北京:清华大学出版社.2007.
[10]焦李成,刘芳,缑水平,刘静,陈莉著.智能数据挖掘与知识发现[M].西安.西安电子科技大学出版社,2006.
[11]周志华,王珏著.机器学习及其应用[M].北京:清华大学出版社.2007.
[12]Mehmed Kantardzic著,闪四清等译.数据挖掘-概念、模型、方法和算法[M].北京:清华大学出版社.2007.
[13]Giudici P.实用数据挖掘[M],袁方等译.北京:人民邮电出版社,2004.
[14]Hand D, Mannila H., Smyth P.数据挖掘原理[M],张银奎等译.北京:机械工业出版社,2003.
[15]Han J, Kamber M.数据挖掘概念与技术[M]范明,孟晓峰等译.北京:机械工业出版社,2001.
[16]Alfredo V. A Methodology for the Characterization of Business-to-Consumer E-commerce[D]. Liverpool, UK: John Moores University,2000.
[17]董彩玲.几种典型数据挖掘方法及其应用研究[D].山东大学,2010.
[18]邓晓梅.基于数据挖掘的电信客户细分模型研究[D].大连理工大学,2006.
[19]王少芬.数据挖掘在电信客户流失模型中的应用[D].厦门大学,2008.
[20]叶郁萌.数据挖掘在移动离网分析模型中的应用[D].北京邮电大学,2006
[21]张宇.在线社会网络信任计算与挖掘分析中若干模型与算法研究[D].浙江大学.2009.
[22]David J H. Intelligent Data Aanalysis:Issue and Opportunities[J]. Intelligent Data Analysis.1998(2)
[23]Wang S H,Wang H.Knowledge Discovery Through self-Organizing Maps[J]:Data Visualization and Query Processing.Knowledge and Information Systems.2002
[24] Kim J,Bentley P J,The artificial immune system for network intrusion detection operator[J],In Proceedings on the Congress on Evolutionary Computation(CEC2001),Seoul,Korea,May,2001
[25] Zhang X R,Wang S, Tan S, et al.Selective SVMs ensemble driven by immune clonal algorithm. LECTURE NOTES IN COMPUTER SCIENCE:2005.
[26] Jiao L C , Li Q.Kernel Matching Pursuit Classifier Ensemble , Pattern Recognition:2006m(44)
[27] Suh E H, Noh K C, Suh C K. Customer List Segmentation Using the Combined Response Model[J]. Expert Systems with Applications,1999,17(2).
[28] Boone D S, Michelle R. Retail Segmentation Using Artificial Neural Networks[J]. International Journal of Research in Marketing, 2002,19(3): 287-301.
[29] Heilman C M, Douglas B. Segmenting Consumers Using Multiple-category Purchase Data[J]. Internernational Journal of Research in Marketing, 2002, 19(3): 225-252.
[30] J. L. Lin, and M. H. Dunham. Mining association rules:Anti-skew algorithms[J]. Proceedings of the International Conference on Data Engineering. Orlando, Florida, February 1998.
[31] J. L. Lin, and M. H. Dunham. Mining association rules:Anti-skew algorithms. Proceedings of the International Conference on Data Engineering. Orlando, Florida, February 1998.
[32] XU J,CHEN H.Criminal network analysis and visualization[J].Communica- tions of the ACM,2005,48(6).
[33]汤小文,蔡庆生.数据挖掘在电信业中的应用[J].计算机工程,2004,6 .
[34]李连祥.中国电信全国计费结算中心BI系统建设[J].通信世界,2003,9.
[35]郭道宁,舒华英.数据挖掘在电信运营市场决策支持中的应用[J].北京邮电大学党报,2004,6.
[36]王琳.数据挖掘技术在通信规划和预测中的应用[J].邮电规划,2004,2.
[37]周志华.机器学习与数据挖掘.中国计算机学会通讯[J].2007,3.
[38]毛勇,周晓波,夏铮,尹征,孙优贤.特征选择算法研究综述[J].模式识别与人工智能,2007,20(2).
[39]任颖,李华伟.一种新型神经网络模型在数据挖掘中的应用[J].现代计算机,2010,3.
[40]刘霞,王爱民.数据挖掘技术在CRM中的应用[J] .现代管理科学,2004,20 (3).
[41]张文宇,陇小渝.电信经济发展规划决策支持系统研究[J].西安邮电学院学报,2000,3(1).
[42]张喆，?鹑唬菩≡?数据挖掘技术在CRM中的应用[J].中国管理科学,2003.
[43]刘英姿,崔南方,马士华.基于电信业务的数据仓库系统设计[J].华中科技大学学报,2002,9.
[44]宋余庆,朱玉全,孙志挥,等.基于FP2Tree的最大频繁项目集挖掘及其更新算法[J].软件学报,2003,14.
[45]吴斌.商业智能的技术与实践[J].中国计算机用户,2004.
[46] ADAFRE S F,DE RIJKE M, Discovering missing links in wikipedia[C]//The 3rd International Workshop on link discovery.2005:90-97.
[2]W.H.Inmon:Building the Data Warehouse. Second Edition [M].1998.
[3]RUD O P.数据挖掘实践[M].北京:机械工业出版社,2003.
[4] AlexBerson,Stephensmith,KurtThearling著.构件面向CRM的数据挖掘应用[M],贺奇等译.北京.人民邮电出版社,2001.
[5]邵峰晶,于忠清.数据挖掘原理与算法[M].北京:中国水利水电出版社,2003.
[6]章兢,数据挖掘算法及其工程应用[M].北京.机械工业出版社,2006.
[7]胡可云,田凤占,黄厚宽.数据挖掘理论与应用[M].北京:清华大学出版社,2008.
[8]章兢,数据挖掘算法及其工程应用[M].北京.机械工业出版社,2008.
[9]周志华,王珏著.机器学习及其应用[M].北京:清华大学出版社.2007.
[10]焦李成,刘芳,缑水平,刘静,陈莉著.智能数据挖掘与知识发现[M].西安.西安电子科技大学出版社,2006.
[11]周志华,王珏著.机器学习及其应用[M].北京:清华大学出版社.2007.
[12]Mehmed Kantardzic著,闪四清等译.数据挖掘-概念、模型、方法和算法[M].北京:清华大学出版社.2007.
[13]Giudici P.实用数据挖掘[M],袁方等译.北京:人民邮电出版社,2004.
[14]Hand D, Mannila H., Smyth P.数据挖掘原理[M],张银奎等译.北京:机械工业出版社,2003.
[15]Han J, Kamber M.数据挖掘概念与技术[M]范明,孟晓峰等译.北京:机械工业出版社,2001.
[16]Alfredo V. A Methodology for the Characterization of Business-to-Consumer E-commerce[D]. Liverpool, UK: John Moores University,2000.
[17]董彩玲.几种典型数据挖掘方法及其应用研究[D].山东大学,2010.
[18]邓晓梅.基于数据挖掘的电信客户细分模型研究[D].大连理工大学,2006.
[19]王少芬.数据挖掘在电信客户流失模型中的应用[D].厦门大学,2008.
[20]叶郁萌.数据挖掘在移动离网分析模型中的应用[D].北京邮电大学,2006
[21]张宇.在线社会网络信任计算与挖掘分析中若干模型与算法研究[D].浙江大学.2009.
[22]David J H. Intelligent Data Aanalysis:Issue and Opportunities[J]. Intelligent Data Analysis.1998(2)
[23]Wang S H,Wang H.Knowledge Discovery Through self-Organizing Maps[J]:Data Visualization and Query Processing.Knowledge and Information Systems.2002
[24] Kim J,Bentley P J,The artificial immune system for network intrusion detection operator[J],In Proceedings on the Congress on Evolutionary Computation(CEC2001),Seoul,Korea,May,2001
[25] Zhang X R,Wang S, Tan S, et al.Selective SVMs ensemble driven by immune clonal algorithm. LECTURE NOTES IN COMPUTER SCIENCE:2005.
[26] Jiao L C , Li Q.Kernel Matching Pursuit Classifier Ensemble , Pattern Recognition:2006m(44)
[27] Suh E H, Noh K C, Suh C K. Customer List Segmentation Using the Combined Response Model[J]. Expert Systems with Applications,1999,17(2).
[28] Boone D S, Michelle R. Retail Segmentation Using Artificial Neural Networks[J]. International Journal of Research in Marketing, 2002,19(3): 287-301.
[29] Heilman C M, Douglas B. Segmenting Consumers Using Multiple-category Purchase Data[J]. Internernational Journal of Research in Marketing, 2002, 19(3): 225-252.
[30] J. L. Lin, and M. H. Dunham. Mining association rules:Anti-skew algorithms[J]. Proceedings of the International Conference on Data Engineering. Orlando, Florida, February 1998.
[31] J. L. Lin, and M. H. Dunham. Mining association rules:Anti-skew algorithms. Proceedings of the International Conference on Data Engineering. Orlando, Florida, February 1998.
[32] XU J,CHEN H.Criminal network analysis and visualization[J].Communica- tions of the ACM,2005,48(6).
[33]汤小文,蔡庆生.数据挖掘在电信业中的应用[J].计算机工程,2004,6 .
[34]李连祥.中国电信全国计费结算中心BI系统建设[J].通信世界,2003,9.
[35]郭道宁,舒华英.数据挖掘在电信运营市场决策支持中的应用[J].北京邮电大学党报,2004,6.
[36]王琳.数据挖掘技术在通信规划和预测中的应用[J].邮电规划,2004,2.
[37]周志华.机器学习与数据挖掘.中国计算机学会通讯[J].2007,3.
[38]毛勇,周晓波,夏铮,尹征,孙优贤.特征选择算法研究综述[J].模式识别与人工智能,2007,20(2).
[39]任颖,李华伟.一种新型神经网络模型在数据挖掘中的应用[J].现代计算机,2010,3.
[40]刘霞,王爱民.数据挖掘技术在CRM中的应用[J] .现代管理科学,2004,20 (3).
[41]张文宇,陇小渝.电信经济发展规划决策支持系统研究[J].西安邮电学院学报,2000,3(1).
[42]张喆，?鹑唬菩≡?数据挖掘技术在CRM中的应用[J].中国管理科学,2003.
[43]刘英姿,崔南方,马士华.基于电信业务的数据仓库系统设计[J].华中科技大学学报,2002,9.
[44]宋余庆,朱玉全,孙志挥,等.基于FP2Tree的最大频繁项目集挖掘及其更新算法[J].软件学报,2003,14.
[45]吴斌.商业智能的技术与实践[J].中国计算机用户,2004.
[46] ADAFRE S F,DE RIJKE M, Discovering missing links in wikipedia[C]//The 3rd International Workshop on link discovery.2005:90-97.
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