Mining top-k frequent closed patterns without minimum support

By Jiawei Han, Jianyong Wang, Ying Lu, P. Tzvetkov. IEEE International Conference on Data Mining. Proceedings, 2002

310 citations recorded by the source corpus.

How researchers see this paper

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Efficiently Using Prefix-trees in Mining Frequent Itemsets (2003, background): “The FP-tree has been shown to be one of the most efficient data structures for mining frequent patterns and for “iceberg” data cube computations [6, 7, 9, 8].”
CLOSET+: searching for the best strategies for mining frequent closed itemsets (2003, methodology): “Inspired by the FP-tree [8] implementation, we can store all the frequent closed itemsets in a compressed result tree which has been used in a slightly different way to do both superset and subset checking [9].”
Mining frequent spatio-temporal sequential patterns (2005, background): “Since a pattern with same frequency to one of its supersets is redundant, we focus on detectingclosedfrequent patterns [4], for which every proper subpattern has equal frequency.”
Fast algorithms for frequent itemset mining using FP-trees (2005, background): “The experimental results reported in [26] showed that CLOSET is faster than CHARM and A-close.”