A tree projection algorithm for generation of frequent itemsets

A tree projection algorithm for generation of frequent itemsets (2000)

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by Ramesh C. Agarwal , Charu C. Aggarwal , V. V. V. Prasad

Venue:	Journal of Parallel and Distributed Computing
Citations:	123 - 0 self

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Datum	Value	Source
TITLE	A tree projection algorithm for generation of frequent itemsets	INFERENCE
AUTHOR NAME	Ramesh C. Agarwal	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM T. J. Watson Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	Yorktown Heights, NY 10598	SVM HeaderParse 0.2
AUTHOR NAME	Charu C. Aggarwal	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM T. J. Watson Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	Yorktown Heights, NY 10598	SVM HeaderParse 0.2
AUTHOR NAME	V. V. V. Prasad	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM T. J. Watson Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	Yorktown Heights, NY 10598	SVM HeaderParse 0.2
ABSTRACT	In this paper we propose algorithms for generation of frequent itemsets by successive construction of the nodes of a lexicographic tree of itemsets. We discuss di erent strategies in generation and traversal of the lexicographic tree such as breadth- rst search, depth- rst search or a combination of the two. These techniques provide di erent trade-o s in terms of the I/O, memory and computational time requirements. We use the hierarchical structure of the lexicographic tree to successively project transactions at each node of the lexicographic tree, and use matrix counting on this reduced set of transactions for nding frequent itemsets. We tested our algorithm on both real and synthetic data. We provide an implementation of the tree projection method which is up to one order of magnitude faster than other recent techniques in the literature. The algorithm has a well structured data access pattern which provides data locality and reuse of data for multiple levels of the cache. We also discuss methods for parallelization of the	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE	Journal of Parallel and Distributed Computing	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	350--371	INFERENCE
VOLUME	61	INFERENCE
CITATIONS	17 found	ParsCit 1.0