A maximal frequent itemset algorithm

被引：0
|
作者：
Wang, H [1 ]
Li, QH [1 ]
Ma, CX [1 ]
Li, KL [1 ]
机构：
[1] Huazhong Univ Sci & Technol, Comp Sch, Wuhan 430074, Peoples R China
来源：
ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING | 2003年 / 2639卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 itemsets, to prune the search space to get the exact set of MFI.
引用
收藏
页码：484 / 490
页数：7
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