Article

LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining

Authors:

Masashi Kiyomi,

Hiroki ArimuraAuthors Info & Claims

OSDM '05: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations

Pages 77 - 86

https://doi.org/10.1145/1133905.1133916

Published: 21 August 2005 Publication History

Abstract

For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. To find all the frequent itemsets, the heaviest task is the computation of frequency of each candidate itemset. In the previous studies, there are roughly three data structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of these has its own advantage and disadvantage with respect to the density of the input database. In this paper, we propose an efficient way to combine these three data structures so that in any case the combination gives the best performance.

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cover image ACM Conferences

OSDM '05: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations

August 2005

92 pages

ISBN:1595932100

DOI:10.1145/1133905

Conference Chairs:
Bart Goethals
University of Antwerp, Belgium
,
Siegfried Nijssen
LIACS, Universiteit Leiden, The Netherlands
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute, NY, USA

Copyright © 2005 ACM.

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Published: 21 August 2005

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