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This paper reports a method that finds out interesting rules from the heterogeneous Internet search histories. Rule extraction aims to improve business performance through an understanding of past and present search histories of customers. A challenging task is to determine interesting rules from their heterogeneous search histories of shopping in the Internet. Customers visit web pages one after another and leave their valuable search information behind. Firstly the authors produce a homogeneous data set from their heterogeneous search histories. It is difficult task to produce a homogeneous data from heterogeneous data without changing their characteristics of data. Secondly these data are trained by unsupervised NN to get their significant classes.
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