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题名: Applying improved clustering algorithm into EC environment data mining
作者: MaYu Peng ; MaBo ; JiangTong Hai
会议名称: 2nd International Conference on Mechatronics and Industrial Informatics, ICMII 2014
会议日期: May 30, 2014 - May 31, 2014
出版日期: 2014
会议地点: Guangzhou, China
出版地: Trans Tech Publications Ltd
收录类别: EI
ISSN: 16609336
ISBN: 9783038351764
部门归属: (1) Research Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang Province, China
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.
语种: 英语
内容类型: 会议论文
URI标识: http://ir.xjipc.cas.cn/handle/365002/3611
Appears in Collections:多语种信息技术研究室_会议论文

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