Extraction of Interesting Rules from Internet Search Histories

摘要

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 we 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. Thirdly, the interesting rules are extracted by inspecting the attributes of customers. These rules are interesting and important for the traders, marketers and customers for making future business plan.