Fast Algorithm for Mining Maximal Frequent Itemsets

Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Most existing work focuses on mining all frequent itemsets. However, since any subset of a frequent set also is frequent, it is sufficient to mine only the set of maximal frequent itemsets. In this paper, we study the performance of two existing approaches, MAFIA and FpMAX, for mining maximal frequent itemsets. We also develop an algorithm, called FMFIA. In this algorithm, we develop and integrate two techniques in order to improve the efficiency of mining maximal frequent itemsets. We also present experimental results which show that our method outperforms the existing methods MAFIA and FpMAX.