基于客户市场细分的电信服务产品设计及优惠规则研究
|
摘要
在竞争的环境下产品是企业的生产力和核心竞争力。电信资费和销售对象是电信服务产品的核心。电信资费是由优惠规则所产生。目前电信运营商都采用自然语言来描述优惠规则。自然语言描述优惠规则易产生歧义和不可能使用计算机实现优惠规则之间的自动化冲突检测。销售对象也称产品的目标客户群。但是,现在也没有一套比较有效的产品目标客户群划分的方法。如何来实现优惠规则的自动化的冲突检测和找出电信服务产品目标客户群方法就成为当前电信服务产品开发和推向市场的关键问题。
1、本文分析了数千种资费规则后应用Lagrange插值法首次得到了结构化的、统一的和简单清晰的优惠规则数学模型表达式,并将优惠规则分解为优惠对象、优惠对象生效域、优惠参考对象、优惠参考对象生效域、优惠方式、生效时间和失效时间等,为优惠规则的冲突检测奠定了基础。
2、本文运用专家系统知识表示将每条优惠规则表示为条件部分和结论部分,采用当前有代表性的冲突检测算法Rete算法和Rete改进算法,首次实现了优惠规则的自动化冲突检测。
3、本文提出了以《因子+运算符+阀值》客户分级规则的灵活定制模型,与聚类算法如K-Means算法、自组织映射神经网络(SOM)算法和平衡递归递减聚类层次算法(BIRCH)的结合,首次提出电信服务产品的客户细分方法,为找出电信服务产品的目标客户群创造了条件。
4、本文在电信服务产品设计中详细描述了可扩展的体系结构、专家分析系统和工作流。专家分析系统实现了优惠规则的冲突检测,工作流实现了产品生命周期的流程化和自动化管理,大大地提高了产品生命周期管理的前瞻性。灵活性和稳定性。
5、本文首次将距离相似度计算运用于电信服务产品用户数和市场指标的计算,为产品推向市场的科学决策提供重要依据。
6、本文完成了电信服务产品的理论分析、算法定制、产品设计与开发以及使用的全过程。使用的电信运营商在产品的科学设置、减少客户投诉、压缩产品数量、提高系统效率和增加经济效益与社会效益等方面都比较满意。
Under the competitive environment, products turn into the productive and competitive power for enterprises. Telecom tariff and sales targets are the kernel of telecom service products. Telecom tariff is generated by discount policies. Presently, telecom operators apply the nature language to explain the policies. It is easy to give different understanding for different people. Moreover it is impossible to use computers to implement automatic conflict detection among them. There is no an effective segmentation method to find customer target group for these products, neither. At present, there are two key problems how to implement automatic conflict detection of the policies and how to find an effective segmentation method of customer marketing for the telecom service products.
1.This dissertation presents a structured, unified, simple and clear mathematical model of the discount policies after analyzing more than thousands of tariff policies by applying the Lagrange interpolation, and also decomposes discount policies into discount objects, operating fields of the objects, discount reference objects, operating fields of the reference objects, discount methods, operating time and invalided time of the policies and so on, which is base of conflict detection of the policies.
2. Each discount policy is expressed by the conditional part and conclusive part with traditional knowledge expression method. By applying the representative algorithms of conflict detections such as Rete and improved Rete algorithms, automatic conflict detections of the policies are implemented in this dissertation.
3. This dissertation presents the agile, conditional customized model, which is consist of basic factors and operators and threshold, and the clustering algorithms, such as K-Means、SOM(Self-Organizing Maps) and BIRCH(Balanced Iterative Reduced Clustering Hierarchy), providing segmenting methods of customer marketing and creating the condition of the finding of target customer base.
4. This dissertation describes expansible hierarchy of the product, expert system and work flow. Expert system has implemented mathematical model of discount policies and conflict detections of tariff policies. Work flow has implemented flowed and automatic management of the whole process. They have enhanced the expansibility, agility and stability in the product cycle life management.
5. This dissertation applies the Distance Measurement into the calculating of telecom service products users and the marketing index, which provides an important evidence for scientific decision-making of products to the market.
6. This dissertation has completed the integrity process of the theory analysis, algorithms customization, product design and development as well as the use of the telecom service products. The operators of using the product that we developed have been satisfied with scientific and rational set up of products, a decrease in customer complaints, compression of the number of products, improved system efficiency and increasing economic and social benefits.
1、本文分析了数千种资费规则后应用Lagrange插值法首次得到了结构化的、统一的和简单清晰的优惠规则数学模型表达式,并将优惠规则分解为优惠对象、优惠对象生效域、优惠参考对象、优惠参考对象生效域、优惠方式、生效时间和失效时间等,为优惠规则的冲突检测奠定了基础。
2、本文运用专家系统知识表示将每条优惠规则表示为条件部分和结论部分,采用当前有代表性的冲突检测算法Rete算法和Rete改进算法,首次实现了优惠规则的自动化冲突检测。
3、本文提出了以《因子+运算符+阀值》客户分级规则的灵活定制模型,与聚类算法如K-Means算法、自组织映射神经网络(SOM)算法和平衡递归递减聚类层次算法(BIRCH)的结合,首次提出电信服务产品的客户细分方法,为找出电信服务产品的目标客户群创造了条件。
4、本文在电信服务产品设计中详细描述了可扩展的体系结构、专家分析系统和工作流。专家分析系统实现了优惠规则的冲突检测,工作流实现了产品生命周期的流程化和自动化管理,大大地提高了产品生命周期管理的前瞻性。灵活性和稳定性。
5、本文首次将距离相似度计算运用于电信服务产品用户数和市场指标的计算,为产品推向市场的科学决策提供重要依据。
6、本文完成了电信服务产品的理论分析、算法定制、产品设计与开发以及使用的全过程。使用的电信运营商在产品的科学设置、减少客户投诉、压缩产品数量、提高系统效率和增加经济效益与社会效益等方面都比较满意。
Under the competitive environment, products turn into the productive and competitive power for enterprises. Telecom tariff and sales targets are the kernel of telecom service products. Telecom tariff is generated by discount policies. Presently, telecom operators apply the nature language to explain the policies. It is easy to give different understanding for different people. Moreover it is impossible to use computers to implement automatic conflict detection among them. There is no an effective segmentation method to find customer target group for these products, neither. At present, there are two key problems how to implement automatic conflict detection of the policies and how to find an effective segmentation method of customer marketing for the telecom service products.
1.This dissertation presents a structured, unified, simple and clear mathematical model of the discount policies after analyzing more than thousands of tariff policies by applying the Lagrange interpolation, and also decomposes discount policies into discount objects, operating fields of the objects, discount reference objects, operating fields of the reference objects, discount methods, operating time and invalided time of the policies and so on, which is base of conflict detection of the policies.
2. Each discount policy is expressed by the conditional part and conclusive part with traditional knowledge expression method. By applying the representative algorithms of conflict detections such as Rete and improved Rete algorithms, automatic conflict detections of the policies are implemented in this dissertation.
3. This dissertation presents the agile, conditional customized model, which is consist of basic factors and operators and threshold
4. This dissertation describes expansible hierarchy of the product, expert system and work flow. Expert system has implemented mathematical model of discount policies and conflict detections of tariff policies. Work flow has implemented flowed and automatic management of the whole process. They have enhanced the expansibility, agility and stability in the product cycle life management.
5. This dissertation applies the Distance Measurement into the calculating of telecom service products users and the marketing index, which provides an important evidence for scientific decision-making of products to the market.
6. This dissertation has completed the integrity process of the theory analysis, algorithms customization, product design and development as well as the use of the telecom service products. The operators of using the product that we developed have been satisfied with scientific and rational set up of products, a decrease in customer complaints, compression of the number of products, improved system efficiency and increasing economic and social benefits.
引文
[1] ITU-T:,TMN Recommendation M.3010(Principle for a Telecommunication Management Network ) TMN Recommendation M.3200( TMN Management Service),ITU-T Recommendations,1996
[2] ITU-T:,TMN Recommendation M.3400(TMN Management Function),ITU-T Recommendations,1997
[3] TMF,Telecom Operation Map (TOM ) Evaluationv2.1,TMFGB910,2000
[4] TMF,Enhanced Telecom Operation Map (eTOM)-The Business Process Framework[s],TMF GB921 v4.0,2004
[5] ITU-T SG4,NGN Management Specification Roadmap ,ITU-T SG4 NGNMFG-OD-010-R2,2005
[6] TMF,NGOSS Release 5.0 Solution Suite Release Notes,TMF C3650,2005
[7]中国移动通信集团公司,中国移动业务运营支撑系统(BOSS)业务技术规范(v1.0),中国移动通信集团企业标准,2001年4月
[8]中国移动通信集团公司,中国移动业务运营支撑系统(BOSS1.5)业务技术规范,中国移动通信企业标准,2004年7月
[9]中国移动通信集团公司,中国移动业务运营支撑系统(BOSS2.0)业务技术规范(讨论稿),中国移动通信企业标准,2006年
[10]中国联通总部计费、结算与信息系统部,中国联通综合营帐系统总体方案,v1.0、中国联通综合营帐系统技术规范,v1.0和中国联通综合营帐系统业务规范,v1.0,中国联通企业标准,2002.10,
[11]中国联通总部计费、结算与信息系统部,中国联通BSS体系结构规划(v1.0试行稿),中国联通企业标准,2004
[12]中国联通市场部,中国联通产品管理业务规范v1.0,中国联通企业标准,2005
[13]中国联通,中国联通IT战略规划,中国联通企业标准,2005
[14]中国联通,中国联通IT系统BSS域:客户关系管理(CRM)业务规范v1.0、中国联通IT系统BSS域:客户关系管理(CRM)技术规范v1.0,中国联通企业标准,2006
[15]中国电信,中国电信CRM评估标准(试行),中国电信企业标准,2006
[16]中国电信,中国电信在线计费系统(OCS)总体技术方案v0.8,中国电信企业标准,2005
[17] Robert B. Doorenbos ,Production Matching for Large Learning Systems,January 31, 1995,CMU-CS-95-113。
[18] Charles L. Forgy, Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem, Artificial Intelligence,,19(1):17–37, 1982
[19] J.K. Kuchar and C.Y. Lee,Survey of Conflict Detection and Resolution Modeling Methods , Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, MA 02139
[20] W.Q. Liu, Steve Easterbrook and John Mylopoulos:Policy-based Detection of Inconsistency in Models,Department of Computer Science,University of Toronto,Toronto,ON M5S 3G4,Canada
[21] E.C. Lupu and M. Sloman, Conflicts in Policy-Based Distributed Systems Management, IEEE Trans on Software Engineering, Vol 25,No. 6,Nov/Dec 1999,pp. 852-869。
[22] R. Sandhu、V. Bhamidipati and Q. Munawer, The ARBAC97 model for role-based admini-stration of roles, ACM Transactions, Inf. Syst. Security, 2(1):105– 135,1999
[23] R. Sandhu、E. Coyne、H. Feinstein and C. Youman.,Role-based access control models, IEEE Computer,29(2):38–47, 1996
[24] R. Sandhu、D. Ferraiolo、and R. Kuhn,The NIST Model for Role-based Access Control towards a Unified Standard,In 5th ACM RBAC,Berlin, Germany, 2000
[25] Rui Xu and Donald Wunsch II.,Survey of clustering algorithm[J]. IEEE Transactions on Neural Networks, 2005,16(3):645-678
[26] Shehroz S.Khan and Amir Ahmad.,Cluster center initialization algorithm for K-means clustering[J], Pattern Recognition Letters,2004(25):1293-1302
[27] Mu Chun Su and Yi-Chun Liu,Two stage algorithm: A new approach to clustering data with arbitrary shapes [J]. Pattern recognition. 2005(38):1887-1901
[28] Jiawei Han and Micheline Kamber,Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Inc. 2001
[29] Wallace C、Dowe D .Intrinsic Classification by MML一the Snob program .In the Proc of the7th Australian Joint Conference on Artificial Intelligence, UNE, Armidale, Australia, World Scientific Publishing Co.,1994:37一44.
[30] Kaufman L and Rousseeuw P.,Finding Groups in Data: An Introductio n to Cluster Analysis.,New York, John Wiley and Sons, NY , 1990:145一193
[31] Raymond T. Ng and Jiawei Han,CLARANS: A Method for Clustering Objects for Spatial Data Mining.,IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 14, NO. 5, SEPTEMBER/OCTOBER 2002: 1003-1016
[32] Ian Davidson, Suny Albany-Technical Report02-2, K-Means No-hierarchical Clustering http://www.cs.albany.edu/~davidson/courses/CS1635/Understanding
[33] Vance Faber.,Clustering and the Continuous k-Means Algorithm. Los Ala mos Science Number 22 1994, http://'www.c3.lanl.gov/~kelly/ ml /pubs/1994esco ncept/sidebar.pdf
[34] F. H?ppner、F. Klawonn and R. Kruse, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis, and Image Recognition. New York: Wiley, 1999
[35] M. Ester、H. Kriegel、J. Sander and X. Xu,A density-based algorithm for discovering clusters in large spatial databases with noise,in Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining (KDD’96), 1996,pp. 226–231.
[36] Hinneburg A and Keim D, An efficient approach to clustering large multi media databases with noise, In Proc, of the 4th ACM SIGKDD, New York, N Y, 1998:58-65
[37] Miheal,Anerst, Markus M. Breunig and Hans-Peter Kriegel and etc. OPTICS: Ordering Points to identify the clustering structure[A] , In: Proceedings of the ACM SIGMOD Conference. Philadephia[C]: ACM Press,1999:49-60
[38] S J. Ester、M. Kriegel and et al,Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications[J]. Data Mining and Knowledge Discovery.1998,2 (2):169-194
[39] Ao ying Zhou、Shuigeng Zhou and Jin Cao etc,Approaches for scaling DBSCAN algorithm to large spatial database[J],Journal of computer science and technology, 2000, 15(06):509-526
[40] H P Kriegel and M Pfeifle,Density-based clustering of uncertain data[A], Proc. 11th Int. Conf. on Knowledge Discovery and Data Mining [C], Chicago, IL, 2005, pp. 672-677
[41] hi-ping Chen、Lei Wang,、Yi-hong Tan.,Clustering Research Based on Density Gradient. WAIM, Seventh International Conference on Web-Age Information Management Workshops, 2006: 15-22.
[42] W Wang、J Yang and R Muntz,STING: a statistical information grid Approach to spatial data mining,In Proc. of the 23rd Conference on VLDB , A thens, G reece, 1997:186一195
[43] S. Guha、R Rastogi、R Shimk., A robust clustering algorithm for categorical attributes. In Proc. of the 15th ICDE,Sydney, A ustralia,19 99:512一521.
[44] R. Agrawal,、J. Gehrke、D. Gunopulos and P. Raghavan,Automatic subspace clustering of high dimensional data for data mining applications,in Proc. ACM SIGMOD Int. Conf. Management of Data, 1998,pp. 94–105.
[45] T. Kohonen,Self-Organizing Maps, Springer-Verlag, New York, 1990.
[46] Huidong Jin、Wing-Ho Shum、Kwong-Sak Leung, etc. Expanding Self-Organizing Map for data visualization and cluster analysis. Information Sciences 163 (2004) 157–173
[47]姜园、张朝阳、仇佩亮等,用于数据挖掘的聚类算法,电子与信息学报,2005,27(4):655-662
[48]蔡颖琨、谢昆青、马修军,屏蔽了输入参数敏感性的DBSCAN改进算法[J],北京大学学报(自然科学版)2004,40(3):480-486
[49]韩家伟、范明、孟小峰,数据挖掘:概念与技术[M],北京:机械工业出版社,2003.
[50]李勇、刘晓东,数据建模技术在电信业务支撑系统中的应用研究,计算机应用。2005,25(9)
[51]杨树莲,数据挖掘在电信行业客户流失分析中的应用,计算机与现代化,2005,114(2):109-111
[52]耿庆鹏、卢子芳,利用数据挖掘技术实现对电信行业用户欺诈行为的预测[J],电信快报,2003(10).
[53]李丙春、耿国华,数据仓库与数据挖掘在电信业中的应用[J],新疆大学学报(自然科学版), 2002(8).
[54]李勇等,基于多元时间序列的移动通信业务产品生命周期评价研究,计算机应用,2006(9)
[55]许安特,如何实现国外先进的计费套装软件,中国计费&OSS大会文集,2005
[56]中国计费网,Cramer6 BSS/OSS套装软件(中文版),中国计费网营运支撑,2007年4月
[57]王燕川等,运营支撑系统标准现状和发展趋势,中兴通讯技术,2006年1期
[58] J. Stoer、R. Bulirsch,Introduction to Numerical Analysis, Springer-Verlag, New York,1993。
[59] J. F.Sowa ,Conceptual graphs for database interface [J ] ,IBM J. Res. & Dev. ,1976,20(4),P336～357。
[60] J. F.Sowa,Conceptual structure:information processing in mind and machine [M] ,Boston ,Addison Wesley Publishing Co. Inc , 1984。
[61]赵来军、党立林,现代数值计算方法,北京大学出版社,2004,7。
[62] Grady Booch、James Rumbaugh and Ivar Jacobson,The Unified Modeling Language User Guide,Addison Wesley, 2000。
[63]孙世新,组合数学(第三版),电子科技大学出版社,2003年
[64] T. Zhang、R. Ramakrishnan and M. Livny,BIRCH: An efficient data clustering method for very large databases,in Proc. ACM SIGMOD Conf. Management of Data, 1996, pp. 103–114.
[65]胡宇舟等,电信用户信用度的计算方法与试验结果,已投通信学报(2007年2月的修改稿)
[66] 20.PAUL D Berger ,NADA I Nasr. Customer lifetime value : marketing models and applications.Journal of Interactive Marketing,1998,12(1):17-30.
[67]李勇,刘晓东.数据建模技术在电信业务支撑系统中的应用研究.计算机应用,2005,(9):2159-2162
[68] .Jiawer Han著,范明等译.数据挖掘概念与技术.北京:机械工业出版社.2001
[69] Joyce Bischoff,Ted Alexander著,成栋等译.数据仓库技术.北京:电子工业出版社.1998
[70].范金城,梅长林.数据分析.北京:科学出版社,2002
[71] .王珊,王能斌,施伯乐,等编著.数据仓库技术与联机分析处理.北京:科学出版社,2001
[72] .陈凤洁.电信客户细分方法及应用.科技和产业,2005,5(11):10-12
[73].崔南方,刘英姿,詹蓉.基于数据仓库技术面向电信服务企业的决策支持系统.科技进步与对策,2003.9,167-168
[74].何月顺,丁秋林.一种有效的优化数据仓库性能的解决方案.南京航空航天大学学报,2004.1,108-111
[75].漆晨曦.电信企业数据仓库的设计和应用.软件世界,2005,(09):80-81
[76].申兵.数据仓库在电信行业中的应用.通信世界.2005,(31).47-48
[77]王尔平,崔宝灵.数据挖掘技术在电信CRM中的应用.管理科学,2003.5,54-58
[78]. Glymour C,Madigan D,Pregibon D,Symth P.Statistical themes and lessons for data mining.Data Mining and Knowledge Discovery,1997,1(1):11-28
[79] .Han Jia wei,Kamber Micheline.Data Mining:Concepts and Techniques [M]. Morgan Kaufman Publishers,Inc,2001.
[80]. Ming-Syan Chen,Jiawei Han and Philip S.Yu,Data Mining:an overview from a database Perspective.IEEE transaction on knowledge and date engineering, December,1996.8(6),866~883
[81]. S Daskalaki,I Kopanas,M Gourdara et al.Data mining for decision support on customer insolvency in telecommunications business .European journal f Operation Research, 2003,145:239-255
[82]. S Daskalaki,I Kopanas,M Gourdara et al. Data mining for decision support on customer insolvency in telecommunications business. European journal of Operation Research, 2003,145:239-255
[83] Aggarwal C C,Yu P S.Data mining techniques for associations, clustering and classification.In:Proc of 3rd Pacific-Asia Conf on Knowledge Discovery in Database.PAKDD-99,Beilin:Spring-Verlag,l999. 13～23
[84]贺玲,吴玲打,蔡益朝等.多媒体数据挖掘中数据间的相似性度量研究[J].国防科技大学学报,2006,28(1):.
[85]杨建武,陈晓欧.基于核矩阵学习的XML文档相似向量方法[J]. Journal of Software, 2006,17(5): 991-1000.
[86] Tao Li,Shenghou Zhu,Mitsunori Ogihara.A New Distributed Data Mining Model Based on Similarity.ACM,2003.
[87] Han Jia wei,Kamber Micheline.Data Mining:Concepts and Techniques[M].San Francisco,CA, USA:Morgan Kaufmann Publishers Inc,2001.
[88]熬成龙,苏英,恭元明.基于相似度的复杂数据对象比较[J].北京理工大学学报,2003,23(5):593-595.
[89] [美]Pang-Ning Tan,Michael Steinbach,Vipin Kumar.数据挖掘导论[M].范明,范宏建等译.北京:人民邮电出版社,2006.
[90]李桂林,陈晓云.关于聚类分析中相似度的讨论[J].计算机工程与应用,2004,31:64-65.
[91]郏宣耀.基于相似性二次度量的高维数据聚类算法[J].计算机应用,2005,25:176-177.
[92]刘纪平,汪宏滨,汪诚波,周洞汝.基于模糊最近邻的高维数据聚类算法[J].小型微型计算机系统,2005,26(2):261-263.
[93] Wilson R.Marinez T.Improved heterogeneous distance functions.Journal of Artificial Intelligence Research,1997,6(1):l～34
[94] H Mannila、H Toivonen,Multiple uses of frequent sets and condensed representatioms , In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD’96), Portland, Oregon, Aug, 1996, AAAI Press, 189-194.
[2] ITU-T:,TMN Recommendation M.3400(TMN Management Function),ITU-T Recommendations,1997
[3] TMF,Telecom Operation Map (TOM ) Evaluationv2.1,TMFGB910,2000
[4] TMF,Enhanced Telecom Operation Map (eTOM)-The Business Process Framework[s],TMF GB921 v4.0,2004
[5] ITU-T SG4,NGN Management Specification Roadmap ,ITU-T SG4 NGNMFG-OD-010-R2,2005
[6] TMF,NGOSS Release 5.0 Solution Suite Release Notes,TMF C3650,2005
[7]中国移动通信集团公司,中国移动业务运营支撑系统(BOSS)业务技术规范(v1.0),中国移动通信集团企业标准,2001年4月
[8]中国移动通信集团公司,中国移动业务运营支撑系统(BOSS1.5)业务技术规范,中国移动通信企业标准,2004年7月
[9]中国移动通信集团公司,中国移动业务运营支撑系统(BOSS2.0)业务技术规范(讨论稿),中国移动通信企业标准,2006年
[10]中国联通总部计费、结算与信息系统部,中国联通综合营帐系统总体方案,v1.0、中国联通综合营帐系统技术规范,v1.0和中国联通综合营帐系统业务规范,v1.0,中国联通企业标准,2002.10,
[11]中国联通总部计费、结算与信息系统部,中国联通BSS体系结构规划(v1.0试行稿),中国联通企业标准,2004
[12]中国联通市场部,中国联通产品管理业务规范v1.0,中国联通企业标准,2005
[13]中国联通,中国联通IT战略规划,中国联通企业标准,2005
[14]中国联通,中国联通IT系统BSS域:客户关系管理(CRM)业务规范v1.0、中国联通IT系统BSS域:客户关系管理(CRM)技术规范v1.0,中国联通企业标准,2006
[15]中国电信,中国电信CRM评估标准(试行),中国电信企业标准,2006
[16]中国电信,中国电信在线计费系统(OCS)总体技术方案v0.8,中国电信企业标准,2005
[17] Robert B. Doorenbos ,Production Matching for Large Learning Systems,January 31, 1995,CMU-CS-95-113。
[18] Charles L. Forgy, Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem, Artificial Intelligence,,19(1):17–37, 1982
[19] J.K. Kuchar and C.Y. Lee,Survey of Conflict Detection and Resolution Modeling Methods , Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, MA 02139
[20] W.Q. Liu, Steve Easterbrook and John Mylopoulos:Policy-based Detection of Inconsistency in Models,Department of Computer Science,University of Toronto,Toronto,ON M5S 3G4,Canada
[21] E.C. Lupu and M. Sloman, Conflicts in Policy-Based Distributed Systems Management, IEEE Trans on Software Engineering, Vol 25,No. 6,Nov/Dec 1999,pp. 852-869。
[22] R. Sandhu、V. Bhamidipati and Q. Munawer, The ARBAC97 model for role-based admini-stration of roles, ACM Transactions, Inf. Syst. Security, 2(1):105– 135,1999
[23] R. Sandhu、E. Coyne、H. Feinstein and C. Youman.,Role-based access control models, IEEE Computer,29(2):38–47, 1996
[24] R. Sandhu、D. Ferraiolo、and R. Kuhn,The NIST Model for Role-based Access Control towards a Unified Standard,In 5th ACM RBAC,Berlin, Germany, 2000
[25] Rui Xu and Donald Wunsch II.,Survey of clustering algorithm[J]. IEEE Transactions on Neural Networks, 2005,16(3):645-678
[26] Shehroz S.Khan and Amir Ahmad.,Cluster center initialization algorithm for K-means clustering[J], Pattern Recognition Letters,2004(25):1293-1302
[27] Mu Chun Su and Yi-Chun Liu,Two stage algorithm: A new approach to clustering data with arbitrary shapes [J]. Pattern recognition. 2005(38):1887-1901
[28] Jiawei Han and Micheline Kamber,Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Inc. 2001
[29] Wallace C、Dowe D .Intrinsic Classification by MML一the Snob program .In the Proc of the7th Australian Joint Conference on Artificial Intelligence, UNE, Armidale, Australia, World Scientific Publishing Co.,1994:37一44.
[30] Kaufman L and Rousseeuw P.,Finding Groups in Data: An Introductio n to Cluster Analysis.,New York, John Wiley and Sons, NY , 1990:145一193
[31] Raymond T. Ng and Jiawei Han,CLARANS: A Method for Clustering Objects for Spatial Data Mining.,IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 14, NO. 5, SEPTEMBER/OCTOBER 2002: 1003-1016
[32] Ian Davidson, Suny Albany-Technical Report02-2, K-Means No-hierarchical Clustering http://www.cs.albany.edu/~davidson/courses/CS1635/Understanding
[33] Vance Faber.,Clustering and the Continuous k-Means Algorithm. Los Ala mos Science Number 22 1994, http://'www.c3.lanl.gov/~kelly/ ml /pubs/1994esco ncept/sidebar.pdf
[34] F. H?ppner、F. Klawonn and R. Kruse, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis, and Image Recognition. New York: Wiley, 1999
[35] M. Ester、H. Kriegel、J. Sander and X. Xu,A density-based algorithm for discovering clusters in large spatial databases with noise,in Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining (KDD’96), 1996,pp. 226–231.
[36] Hinneburg A and Keim D, An efficient approach to clustering large multi media databases with noise, In Proc, of the 4th ACM SIGKDD, New York, N Y, 1998:58-65
[37] Miheal,Anerst, Markus M. Breunig and Hans-Peter Kriegel and etc. OPTICS: Ordering Points to identify the clustering structure[A] , In: Proceedings of the ACM SIGMOD Conference. Philadephia[C]: ACM Press,1999:49-60
[38] S J. Ester、M. Kriegel and et al,Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications[J]. Data Mining and Knowledge Discovery.1998,2 (2):169-194
[39] Ao ying Zhou、Shuigeng Zhou and Jin Cao etc,Approaches for scaling DBSCAN algorithm to large spatial database[J],Journal of computer science and technology, 2000, 15(06):509-526
[40] H P Kriegel and M Pfeifle,Density-based clustering of uncertain data[A], Proc. 11th Int. Conf. on Knowledge Discovery and Data Mining [C], Chicago, IL, 2005, pp. 672-677
[41] hi-ping Chen、Lei Wang,、Yi-hong Tan.,Clustering Research Based on Density Gradient. WAIM, Seventh International Conference on Web-Age Information Management Workshops, 2006: 15-22.
[42] W Wang、J Yang and R Muntz,STING: a statistical information grid Approach to spatial data mining,In Proc. of the 23rd Conference on VLDB , A thens, G reece, 1997:186一195
[43] S. Guha、R Rastogi、R Shimk., A robust clustering algorithm for categorical attributes. In Proc. of the 15th ICDE,Sydney, A ustralia,19 99:512一521.
[44] R. Agrawal,、J. Gehrke、D. Gunopulos and P. Raghavan,Automatic subspace clustering of high dimensional data for data mining applications,in Proc. ACM SIGMOD Int. Conf. Management of Data, 1998,pp. 94–105.
[45] T. Kohonen,Self-Organizing Maps, Springer-Verlag, New York, 1990.
[46] Huidong Jin、Wing-Ho Shum、Kwong-Sak Leung, etc. Expanding Self-Organizing Map for data visualization and cluster analysis. Information Sciences 163 (2004) 157–173
[47]姜园、张朝阳、仇佩亮等,用于数据挖掘的聚类算法,电子与信息学报,2005,27(4):655-662
[48]蔡颖琨、谢昆青、马修军,屏蔽了输入参数敏感性的DBSCAN改进算法[J],北京大学学报(自然科学版)2004,40(3):480-486
[49]韩家伟、范明、孟小峰,数据挖掘:概念与技术[M],北京:机械工业出版社,2003.
[50]李勇、刘晓东,数据建模技术在电信业务支撑系统中的应用研究,计算机应用。2005,25(9)
[51]杨树莲,数据挖掘在电信行业客户流失分析中的应用,计算机与现代化,2005,114(2):109-111
[52]耿庆鹏、卢子芳,利用数据挖掘技术实现对电信行业用户欺诈行为的预测[J],电信快报,2003(10).
[53]李丙春、耿国华,数据仓库与数据挖掘在电信业中的应用[J],新疆大学学报(自然科学版), 2002(8).
[54]李勇等,基于多元时间序列的移动通信业务产品生命周期评价研究,计算机应用,2006(9)
[55]许安特,如何实现国外先进的计费套装软件,中国计费&OSS大会文集,2005
[56]中国计费网,Cramer6 BSS/OSS套装软件(中文版),中国计费网营运支撑,2007年4月
[57]王燕川等,运营支撑系统标准现状和发展趋势,中兴通讯技术,2006年1期
[58] J. Stoer、R. Bulirsch,Introduction to Numerical Analysis, Springer-Verlag, New York,1993。
[59] J. F.Sowa ,Conceptual graphs for database interface [J ] ,IBM J. Res. & Dev. ,1976,20(4),P336～357。
[60] J. F.Sowa,Conceptual structure:information processing in mind and machine [M] ,Boston ,Addison Wesley Publishing Co. Inc , 1984。
[61]赵来军、党立林,现代数值计算方法,北京大学出版社,2004,7。
[62] Grady Booch、James Rumbaugh and Ivar Jacobson,The Unified Modeling Language User Guide,Addison Wesley, 2000。
[63]孙世新,组合数学(第三版),电子科技大学出版社,2003年
[64] T. Zhang、R. Ramakrishnan and M. Livny,BIRCH: An efficient data clustering method for very large databases,in Proc. ACM SIGMOD Conf. Management of Data, 1996, pp. 103–114.
[65]胡宇舟等,电信用户信用度的计算方法与试验结果,已投通信学报(2007年2月的修改稿)
[66] 20.PAUL D Berger ,NADA I Nasr. Customer lifetime value : marketing models and applications.Journal of Interactive Marketing,1998,12(1):17-30.
[67]李勇,刘晓东.数据建模技术在电信业务支撑系统中的应用研究.计算机应用,2005,(9):2159-2162
[68] .Jiawer Han著,范明等译.数据挖掘概念与技术.北京:机械工业出版社.2001
[69] Joyce Bischoff,Ted Alexander著,成栋等译.数据仓库技术.北京:电子工业出版社.1998
[70].范金城,梅长林.数据分析.北京:科学出版社,2002
[71] .王珊,王能斌,施伯乐,等编著.数据仓库技术与联机分析处理.北京:科学出版社,2001
[72] .陈凤洁.电信客户细分方法及应用.科技和产业,2005,5(11):10-12
[73].崔南方,刘英姿,詹蓉.基于数据仓库技术面向电信服务企业的决策支持系统.科技进步与对策,2003.9,167-168
[74].何月顺,丁秋林.一种有效的优化数据仓库性能的解决方案.南京航空航天大学学报,2004.1,108-111
[75].漆晨曦.电信企业数据仓库的设计和应用.软件世界,2005,(09):80-81
[76].申兵.数据仓库在电信行业中的应用.通信世界.2005,(31).47-48
[77]王尔平,崔宝灵.数据挖掘技术在电信CRM中的应用.管理科学,2003.5,54-58
[78]. Glymour C,Madigan D,Pregibon D,Symth P.Statistical themes and lessons for data mining.Data Mining and Knowledge Discovery,1997,1(1):11-28
[79] .Han Jia wei,Kamber Micheline.Data Mining:Concepts and Techniques [M]. Morgan Kaufman Publishers,Inc,2001.
[80]. Ming-Syan Chen,Jiawei Han and Philip S.Yu,Data Mining:an overview from a database Perspective.IEEE transaction on knowledge and date engineering, December,1996.8(6),866~883
[81]. S Daskalaki,I Kopanas,M Gourdara et al.Data mining for decision support on customer insolvency in telecommunications business .European journal f Operation Research, 2003,145:239-255
[82]. S Daskalaki,I Kopanas,M Gourdara et al. Data mining for decision support on customer insolvency in telecommunications business. European journal of Operation Research, 2003,145:239-255
[83] Aggarwal C C,Yu P S.Data mining techniques for associations, clustering and classification.In:Proc of 3rd Pacific-Asia Conf on Knowledge Discovery in Database.PAKDD-99,Beilin:Spring-Verlag,l999. 13～23
[84]贺玲,吴玲打,蔡益朝等.多媒体数据挖掘中数据间的相似性度量研究[J].国防科技大学学报,2006,28(1):.
[85]杨建武,陈晓欧.基于核矩阵学习的XML文档相似向量方法[J]. Journal of Software, 2006,17(5): 991-1000.
[86] Tao Li,Shenghou Zhu,Mitsunori Ogihara.A New Distributed Data Mining Model Based on Similarity.ACM,2003.
[87] Han Jia wei,Kamber Micheline.Data Mining:Concepts and Techniques[M].San Francisco,CA, USA:Morgan Kaufmann Publishers Inc,2001.
[88]熬成龙,苏英,恭元明.基于相似度的复杂数据对象比较[J].北京理工大学学报,2003,23(5):593-595.
[89] [美]Pang-Ning Tan,Michael Steinbach,Vipin Kumar.数据挖掘导论[M].范明,范宏建等译.北京:人民邮电出版社,2006.
[90]李桂林,陈晓云.关于聚类分析中相似度的讨论[J].计算机工程与应用,2004,31:64-65.
[91]郏宣耀.基于相似性二次度量的高维数据聚类算法[J].计算机应用,2005,25:176-177.
[92]刘纪平,汪宏滨,汪诚波,周洞汝.基于模糊最近邻的高维数据聚类算法[J].小型微型计算机系统,2005,26(2):261-263.
[93] Wilson R.Marinez T.Improved heterogeneous distance functions.Journal of Artificial Intelligence Research,1997,6(1):l～34
[94] H Mannila、H Toivonen,Multiple uses of frequent sets and condensed representatioms , In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD’96), Portland, Oregon, Aug, 1996, AAAI Press, 189-194.