Fuzzy classification in web usage mining using fuzzy quantifiers

This paper proposes a new algorithm-FC-WPath, a fuzzy rule-based classification of weighted path traversals using fuzzy quantifiers for web usage mining. Web usage mining usually analyses frequent path traversals or frequent subgraphs where each path has the same level of importance. However, the current frequent pattern mining based methods could not distinguish the level of importance for different paths. Further, there is little work done in relation to classification of path traversals based on fuzzy classification inferences and fuzzy quantification, which often provides good readability and interpretation of complex patterns. In this work, we attach numeric weights to each path traversed according to some level of importance, therefore introducing quantitative and fuzzy values. The derived fuzzy if-then classification rules from weighted paths can then be described both by the linguistic fuzzy rules and linguistic quantifiers like "all", "some" etc. As a result, we propose a fuzzy subset-hood model with fuzzy quantifiers for describing the usual fuzzy if-then rules applied to web usage mining. The experimental result shows that the proposed FC-WPath algorithm has good classification accuracy, readability and runtime.