SmartMiner: A Depth First Algorithm Guided by Tail Information for Mining Maximal Frequent Itemsets

Authors:
Qinghua Zou;Wesley W. Chu;Baojing Lu
Affiliations:
-;-;-
Venue:
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Year:
2002

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Abstract

Maximal frequent itemsets (MFI) are crucial to manytasks in data mining. Since the MaxMiner algorithm firstintroduced enumeration trees for mining MFI in 1998,several methods have been proposed to use depth firstsearch to improve performance. To further improve theperformance of mining MFI, we proposed a techniquethat takes advantage of the information gathered fromprevious steps to discover new MFI. More specifically,our algorithm called SmartMiner gathers and passes tailinformation and uses a heuristic select function whichuses the tail information to select the next node toexplore. Compared with Mafia and GenMax, SmartMinergenerates a smaller search tree, requires a smallernumber of support counting, and does not requiresuperset checking. Using the datasets Mushroom andConnect, our experimental study reveals that SmartMinergenerates the same MFI as Mafia and GenMax, but yieldsan order of magnitude improvement in speed.