Research on Segmentation of E-shoppers Based on Clustering
With the rapid development of online shopping, the ability to segment e-shoppers basing on their preferences and characteristics has become a key source of competitive advantage for firms. This paper presented the realistic algorithms for clustering e-shoppers in e-commerce applications. Multi-dimensional range search is presented to solve the range-searching problem. This is a multilevel structure since its nodes have pointers to associated structures. In addition, in this paper, the global k-means algorithm is presented which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure The basic idea underlying the proposed method is that an optimal solution for a clustering problem with M clusters can be obtained using a series of local searches (using the k-means algorithm). The method is independent of any starting conditions. The better result is achieved by applying the two new algorithms to a given database for e-shoppers.
e-shopper segmentaton dataset dimension cluster
Wang Chong Liu Jian Wang Yanqing
Huaihai Institute of Technology, Lianyungang, Jiangsu, 222000,China Harbin Institute of Teclmology, Huaihai Institute of Technology, Lianyungang, Jiangsu, 222000,China Harbin Institute of Teclmology, Harbin, Heilongjiang, 150001,China
国际会议
长沙
英文
2462-2465
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）