Mining generalized fuzzy association rules from web taxonomic Mining generalized fuzzy association rules from web taxonomic

Abstract

The discovery of fuzzy association rules is an important data-mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient method named cluster-based fuzzy association rule (CBFAR) to discover generalized fuzzy association rules from web structures. The CBFAR method is to create fuzzy cluster tables by scanning the browse information database (BIDB) once, and then clustering the browse records to the k-th cluster table, where the length of a record is k. The counts of the fuzzy regions are stored in the Fuzzy_Cluster Tables. This method requires less contrast to generate large itemsets. The CBFAR method is also discussed.