Presentation	2003/7/10 Parallel FP-growth Algorithm for Frequent Pattern Mining Eigo IWAHASHI, Hayato YAMANA,
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Abstract(in English)	Frequent patterns mining is one of the important problem in data mining research. The Apriori is a prominent algorithm followed by many variants. In 2000, the FP-growth, which is reported to be faster than the Apriori, was proposed. However, many parallel algorithms of frequent pattern mining are still based on the Apriori. In this paper, we propose a parallelized version of the FP-growth, which accesses disks in parallel and constructs local FP-trees on each local memory. As a result of the evaluation using 32 node PC cluster, our method is approximately 2 and 130 times faster than sequential FP-growth, when minimum support is 0.25% and 2%, respectively.
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Keyword(in English)	Data Mining / Frequent Pattern / Parallel Processing / FP-growth / PC Cluster
Paper #	DE2003-50
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Conference Information
Committee	DE
Conference Date	2003/7/10(1days)
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Paper Information
Registration To	Data Engineering (DE)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Parallel FP-growth Algorithm for Frequent Pattern Mining
Sub Title (in English)
Keyword(1)	Data Mining
Keyword(2)	Frequent Pattern
Keyword(3)	Parallel Processing
Keyword(4)	FP-growth
Keyword(5)	PC Cluster
1st Author's Name	Eigo IWAHASHI
1st Author's Affiliation	Graduate School of Science and Engineering, Waseda University()
2nd Author's Name	Hayato YAMANA
2nd Author's Affiliation	Faculty of Science and Engineering, Waseda University
Date	2003/7/10
Paper #	DE2003-50
Volume (vol)	vol.103
Number (no)	191
Page	pp.pp.-
#Pages	6
Date of Issue