Applying Improved Clustering Algorithm into EC Environment Data Mining

• Published in: Applied Mechanics and Materials Vol. 596; no. Mechatronics and Industrial Informatics II; pp. 951 - 959
• Main Authors: Jiang, Tong Hai, Ma, Bo, Ma, Yu Peng
• Format: Journal Article
• Language: English
• Published: Zurich Trans Tech Publications Ltd 01.07.2014
• Subjects: Algorithms; Clustering; Customers; Data mining; Feasibility; Learning; On-line systems; Tasks; EC Environment; Improved K-Means; K-Mean; Customer Segmentation
• ISBN: 9783038351764; 3038351768
• ISSN: 1660-9336; 1662-7482; 1662-7482
• DOI: 10.4028/www.scientific.net/AMM.596.951

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.