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.
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