The E-Shopping Experience has opened the new ways of business and shopping. The conventional terms of shopping have been changed and new terms to shop online emerge into customers' online shopping behaviors and preferences. Extort interesting shopping patterns from ever increasing data is not a inconsequential mission. It require intelligent association rule mining of the available data, that can be practically knowledgeable for the online retail stores, so that they can make viable business decisions .The fast 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 pragmatic algorithms for clustering e-shoppers in e-commerce applications. Various multidimensional range search is presented to solve the range-searching problem..In addition, in this paper, the global clustering 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 finest solution for a clustering problem with other clusters can be obtained using a series of location based clustering and segmentation.