Mining frequent itemsets with partial enumeration

Authors:
Peiyi Tang;Markus P. Turkia
Affiliations:
University of Arkansas at Little Rock, Little Rock, AR;University of Arkansas at Little Rock, Little Rock, AR
Venue:
Proceedings of the 44th annual Southeast regional conference
Year:
2006

Citing 3
Cited 1

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints

IEEE Transactions on Knowledge and Data Engineering
Domain and data partitioning for parallel mining of frequent closed itemsets

Proceedings of the 43rd annual Southeast regional conference - Volume 1

Mining frequent web access patterns with partial enumeration

ACM-SE 45 Proceedings of the 45th annual southeast regional conference

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Abstract

In this paper, we present an algorithm of mining frequent itemsets using partial enumeration and the FP-growth function with reduced depth of recursion. The experimental results show that our algorithm outperforms the original FP-growth algorithm without partial enumeration for the databases with high density.