Discovery of Fuzzy Multiple-Level Web Browsing Patterns

Abstract

Web usage mining is the application of data mining techniques to discover usage patterns from web data. It can be used to better understand web usage and better serve the needs of rapidly growing web–based applications. Discovery of browsing patterns, page clusters, user clusters, association rules and usage statistics are some usage patterns in the web domain. Web mining of browsing patterns including simple sequential patterns and sequential patterns with browsing times has been studied recently. However, most of these works focus on mining browsing patterns of web pages directly. In this work, we introduce the problem of mining browsing patterns on multiple levels of a taxonomy comprised of web pages. The browsing time on each web page is used to analyze the retrieval behavior. Since the data collected are numeric, fuzzy concepts are used to process them and to form linguistic terms. A web usage–mining algorithm to discover multiple–level browsing patterns from linguistic data is thus proposed. Each page uses only the linguistic term with maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of pages. Computation time can thus be greatly reduced. In addition, the inclusion of concept hierarchy (taxonomy) of web pages produces browsing patterns of different granularity. This allows the views of users’ browsing behavior from various levels of perspectives.