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Applying improved clustering algorithm into EC environment data mining
MaYu Peng; MaBo; JiangTong Hai
Conference Name2nd International Conference on Mechatronics and Industrial Informatics, ICMII 2014
Conference DateMay 30, 2014 - May 31, 2014
Conference PlaceGuangzhou, China
Publication PlaceTrans Tech Publications Ltd

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.

KeywordEc Environment Customer Segmentation K-means Improved K-means.
Indexed ByEI
Document Type会议论文
AffiliationResearch Center for Multilingual Information Technology, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang Province, China
Recommended Citation
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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