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Applying improved clustering algorithm into EC environment data mining
MaYu Peng; MaBo; JiangTong Hai
2014
会议名称2nd International Conference on Mechatronics and Industrial Informatics, ICMII 2014
页码951-959
会议日期May 30, 2014 - May 31, 2014
会议地点Guangzhou, China
出版地Trans Tech Publications Ltd
摘要

With the rising growth of electronic commerce (EC) customers, EC service providers are keen to analyze the on-line browsing behavior of the customers in their web site and learn their specific features. Clustering is a popular non-directed learning data mining technique for partitioning a dataset into a set of clusters. Although there are many clustering algorithms, none is superior for the task of customer segmentation. This suggests that a proper clustering algorithm should be generated for EC environment. In this paper we are concerned with the situation and proposed an improved k-means algorithm, which is effective to exclude the noisy data and improve the clustering accuracy. The experimental results performed on real EC environment are provided to demonstrate the effectiveness and feasibility of the proposed approach.

关键词Ec Environment Customer Segmentation K-means Improved K-means.
收录类别EI
文献类型会议论文
条目标识符http://ir.xjipc.cas.cn/handle/365002/3611
专题多语种信息技术研究室
作者单位Research Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang Province, China
推荐引用方式
GB/T 7714
MaYu Peng,MaBo,JiangTong Hai. Applying improved clustering algorithm into EC environment data mining[C]. Trans Tech Publications Ltd,2014:951-959.
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