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A new algorithm for fast mining frequent itemsets using N-lists

science china(Information sciences) 2012年第9期55卷 2008-2030页

作者： deng zhihong ,wang zhonghui & jiang jiajian key laboratory of machine perception(Ministry of Education),school of electronics engineering and computer science,peking university,beijing 100871,china key laboratory of machine perception (Ministry of Education) School of Electronics Engineering and Computer Science Peking University Beijing China

Mining frequent itemsets has emerged as a fundamental problem in data mining and plays an essential role in many important data mining *** this paper,we propose a novel vertical data representation called N-list,which originates from an FP-tree-like coding prefix tree called PPC-tree that stores crucial information about frequent *** on the N-list data structure,we develop an efficient mining algorithm,PrePost,for mining all frequent *** of PrePost is achieved by the following three ***,N-list is compact since transactions with common prefixes share the same nodes of the ***,the counting of itemsets’ supports is transformed into the intersection of N-lists and the complexity of intersecting two N-lists can be reduced to O(m + n) by an efficient strategy,where m and n are the cardinalities of the two N-lists ***,PrePost can directly find frequent itemsets without generating candidate itemsets in some cases by making use of the single path property of *** have experimentally evaluated PrePost against four state-of-the-art algorithms for mining frequent itemsets on a variety of real and synthetic *** experimental results show that the PrePost algorithm is the fastest in most *** though the algorithm consumes more memory when the datasets are sparse,it is still the fastest one.

关键词： data mining frequent itemset mining data structure N-lists algorithm

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