[1]李爱群 乔晗 王汝传 邓松.基于分布式混合数据挖掘的电信客户流失分析[J].计算机技术与发展,2010,(10):43-46.
　LI Ai-qun,QIAO Han,WANG Ru-chuan,et al.Telecommunication Carriers Customer Churn Analysis Based on Distributed Hybrid Data Mining[J].,2010,(10):43-46.
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基于分布式混合数据挖掘的电信客户流失分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2010年10期
页码:
43-46
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Telecommunication Carriers Customer Churn Analysis Based on Distributed Hybrid Data Mining
文章编号:
1673-629X（2010）10-0043-04
作者:
李爱群1; 2 乔晗1 王汝传1; 2 邓松1
[1]南京邮电大学计算机学院[2]南京邮电大学计算机研究所
Author(s):
LI Ai-qun; QIAO Han; WANG Ru-chuan;  DENG Song
[1]College of Computer,Nanjing University of Posts and Telecommunications[2]Institute of Computer,Nanjing University of Posts and Telecommunications
关键词:
客户流失分析; 网格计算; BP神经网络; K-Means聚类算法
Keywords:
customer churn analysis;  grid computing;  BP neural network;  K-Means clustering algorithm
分类号:
TP39
文献标志码:
A
摘要:
CORBA技术庞大而复杂,且技术和标准的更新相对较慢。电信运营企业应用系统是客户流失分析的主要数据来源,而传统的客户流失分析由于该系统数据的集中式存储继而采用集中式挖掘,对海量数据的挖掘效率低下。为进一步提高挖掘效率,提出网格下基于分布式混合数据挖掘的电信客户流失分析（Customer Churn Analysis upon Distributed HybridData Mining in Grid,CCA-DHDM）,并借助GridSphere门户,在该平台上实现了BP神经网络算法和K-Means聚类算
Abstract:
CORBA is a large and complex technology,and the updating of technique and standard is relatively slow.Telecom enterprise application systems are the main source of data for customer churn analysis,the traditional customers churn analysis uses a centralized mining due to centralized data storage,and the mining efficiency for mass data is low.Present CCA-DHDM（Customer Churn Analysis upon Distributed Hybrid Data Mining in Grid）,and achieve the BP neural network algorithm and K-Means clustering algorithm in this platform by means of GridSphere Portal.The simulation shows that,compared with stand-alone environment,the average time-consuming of algorithm is decreased obviously 65 percent to 75 percent with the grid nodes increasing,and the efficiency of the algorithm is improved clearly

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备注/Memo

备注/Memo:
国家自然科学基金（60973139,60903181,60773041）; 江苏省自然科学基金（BK2008451）; 江苏省级现代服务业发展专项资金（2010002）; 江苏省高校自然科学基础研究项目（09KJB520009）; 国家和江苏省博士后基金（0801019C,20090451240,20090451241）; 江苏高校科技创新计划项目（CX09B-153Z,CX08B-086Z）; 江苏省六大高峰人才项目（2008118）; 江苏省计算机信息处理技术重点实验室基金（2010）李爱群（1969
更新日期/Last Update: 1900-01-01