Fast algorithms for mining multiple fuzzy frequent itemsets
Jerry Chun‐Wei Lin, Ting Li, Philippe Fournier‐Viger, Tzung‐Pei Hong, Ja-Hwung Su · 2016
In the past, several algorithms were developed to mine fuzzy frequent itemsets (FFIs) in which each item is represented at most one linguistic term based on maximum scalar cardinality. In real-life situations, multiple fuzzy linguistic terms instead of the single one can, however, produce more useful and meaningful fuzzy association rules. The Apriori-based algorithm was developed to mine multiple fuzzy frequent itemsets (MFFIs), which requires to generate the amounts of candidates and determine them in a level-wise way. In this paper, a fuzzy-list-based (FL)-Miner algorithm is developed to mine the complete set of MFFIs without candidate generation. Two efficient pruning strategies are also developed to reduce the search space, thus speeding up the mining process to directly discover the MFFIs. Experiments are conducted to show the performance of the proposed approaches compared to the state-of-the-art level-wise algorithm in terms of execution time and memory usage.