A maximal frequent itemset algorithm

Authors:
Hui Wang;Qinghua Li;Chuanxiang Ma;Kenli Li
Affiliations:
Computer School, Huazhong University of Science and Technology, WuHan, P.R.China;Computer School, Huazhong University of Science and Technology, WuHan, P.R.China;Computer School, Huazhong University of Science and Technology, WuHan, P.R.China;Computer School, Huazhong University of Science and Technology, WuHan, P.R.China
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Abstract

We present MinMax, a new algorithm for mining maximal frequent itemsets(MFI) from a transaction database. It is based on depth-first traversal and iterative. It combines a vertical tidset representation of the database with effective pruning mechanisms. MinMax removes all the non-maximal frequent itemsets to get the exact set of MFI directly, needless to enumerate all the frequent itemsets from smaller ones step by step. It backtracks to the proper ancestor directly, needless level by level . We found MinMax to be more effective than GenMax, a state-of-the-art algorithm for finding maximal frequent item-sets, to prune the search space to get the exact set of MFI.