Mining weighted browsing patterns with linguistic minimum supports
Tzung‐Pei Hong, Ming-Jer Chiang, Shyue-Liang Wang · 2003
All the web pages are usually assumed to have the same importance in web mining. Different web pages In a web site may, however, have different importance to users in real applications. Besides, the mining parameters In most conventional data-mining algorithms are numerical. This paper thus attempts to propose a weighted web-mining technique to discover linguistic browsing patterns from log data in web servers. Web pages are first evaluated by managers as linguistic terms to reflect their Importance, which are then transformed as fuzzy sets of weights. Linguistic minimum supports are assigned by users, showing a more natural way of human reasoning. Fuzzy operations including fuzzy ranking are then used to find linguistic weighted large sequences. An example is also given to clearly illustrate the proposed approach.