The research on the data mining technology in the active demand management

摘要：

The traditional K-Means algorithm is sensitive to outliers,outliers traction and easy off-center,and overlap of classes can not very well show their classification.This paper introduces a variant of the probability distribution theory,K-Means clustering algorithm - Gaussian mixture model to part of the customer data randomly selected of Volkswagen dealer in a Beijing office in 2008,for example,and carry out empirical study based on the improved clustering algorithm model.The results showed that: data mining clustering algorithm in active demand management and market segmentation has important significance.

关键词： data mining K-Means algorithm active demand management

作者: CHEN Xuemei GAO Li Wang Xi WEI Zhonghua Zhang Zhenhua Liao Zhigao

作者单位: Beijing institute of technology School of Mechanical and vehicular Engineering Beijing Institute of Beijing institute of technology School of Mechanical and vehicular Engineering Beijing Institute of Beijing University of Technology Traffic Engineering Key Lab of Beijing Beijing University of Techno School of Mechanical and vehicular General Station of Quality Inspection for Special Engineering of

会议类型: 国际会议

会议名称: 2010 4th International Conference on Intelligent Information Techonlogy Application(第四届智能信息技术应用国际学术研讨会 IITA 2010)

会议地点: 秦皇岛

会议语种:英文

页码: 296-300

在线出版日期: 2010-11-05（万方平台首次上网日期，不代表论文的发表时间）