Efficient Mining of Multiple Fuzzy Frequent Item sets

Document Type



Traditional association-rule mining or frequent item set mining only can handle binary databases, in which each item or attribute is represented as either 0 or 1. Several algorithms were developed extensively to discover fuzzy frequent item sets by adopting the fuzzy set theory to the quantitative databases. Most of them considered the maximum scalar cardinality to find, at most, one represented item from the transformed linguistic terms. This paper presents an MFFI-Miner algorithm to mine the complete set of multiple fuzzy frequent item sets (MFFIs) without candidate generation. An efficient fuzzy-list structure was designed to keep the essential information for mining process, which can greatly reduce the computation of a database scan. Two efficient pruning strategies are developed to reduce the search space, thus speeding up the mining process to discover MFFIs directly. Substantial experiments were conducted to compare the performance of the proposed algorithm to the state-of-the-art approaches in terms of execution time, memory usage, and node analysis

UNLV article access

Find in your library