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Proceedings of 2009 International Symposium on Computer Science and Computational Technology (ISCSCT 2009)

Huangshan, China, December 26-28, 2009

Editors: Fei Yu, Guangxue Yue, Jian Shu, Yun Liu

AP Catalog Number: AP-PROC-CS-09CN005

ISBN: 978-952-5726-07-7 (Print), 978-952-5726-08-4 (CD-ROM)

Page(s): 12-15

An Algorithm for Mining Maximum Frequent Itemsets Using Data-sets Condensing and Intersection Pruning

Shui Wang, Ying Zhan, and Le Wang

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Discovering maximal frequent itemset is a key issue in data mining; the Apriori-like algorithms use candidate itemsets generating/testing method, but this approach is highly time-consuming. To look for an algorithm that can avoid the generating of vast volume of candidate itemsets, nor the generating of frequent pattern tree, DCIP algorithm uses data-set condensing and intersection pruning to find the maximal frequent itemset. The condensing process is performed by deleting items in infrequent 1-itemset and merging duplicate transactions repeatedly; the pruning process is performed by generating intersections of transactions and deleting unneeded subsets recursively. This algorithm differs from all classical maximal frequent itemset discovering algorithms; experiments show that this algorithm is valid with moderate efficiency; it is also easy to code for use in KDD applications.

Index Terms

data mining, maximum frequent itemsets, candidate itemsets, intersection pruning, data-set condensing

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