Mining Fuzzy Weighted Browsing Patterns from Time Duration and with Linguistic Thresholds

Abstract

World-wide-web applications have grown very rapidly and have made a significant impact on computer systems. Among them, web browsing for useful information may be most commonly seen. Due to its tremendous amounts of use, efficient and effective web retrieval has become a very important research topic in this field. Techniques of web mining have thus been requested and developed to achieve this purpose. In this research, a new fuzzy weighted web-mining algorithm is proposed, which can process web-server logs to discover useful users’ browsing behaviors from the time durations of the paged browsed. Since the time durations are numeric, fuzzy concepts are used here to process them and to form linguistic terms. Besides, different web pages may have different importance. The importance of web pages are evaluated by managers as linguistic terms, which are then transformed and averaged as fuzzy sets of weights. Each linguistic term is then weighted by the importance for its page. Only the linguistic term with the maximum cardinality for a page is chosen in later mining processes, thus reducing the time complexity. The minimum support is set linguistic, which is more natural and understandable for human beings. An example is given to clearly illustrate the proposed approach.