{'fraction_non_alphanumeric': 0.02029520295202952, 'fraction_numerical': 0.0, 'mean_word_length': 5.06145251396648, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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 multi-level 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.