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Hsinchu, Taiwan
Dec. 5, 2007 to Dec. 7, 2007
ISBN: 978-1-4244-1889-3
pp: 1-8
null Bundit Manaskasemsak , MIKE Lab., Dept. of Computer Engineering, Kasetsart Univ., Bangkok 10900, Thailand
null Nunnapus Benjamas , HPCNC Lab., Dept. of Computer Engineering, Kasetsart Univ., Bangkok 10900, Thailand
Arnon Rungsawang , MIKE Lab., Dept. of Computer Engineering, Kasetsart Univ., Bangkok 10900, Thailand
null Athasit Surarerks , ELITE Lab., Dept. of Computer Engineering, Chulalongkorn Univ., Bangkok 10330, Thailand
null Putchong Uthayopas , HPCNC Lab., Dept. of Computer Engineering, Kasetsart Univ., Bangkok 10900, Thailand
ABSTRACT
Association rule mining is one of the most important techniques in data mining. It extracts significant patterns from transaction databases and generates rules used in many decision support applications. Many organizations such as industrial, commercial, or even scientific sites may produce large amount of transactions and attributes. Mining effective rules from such large volumes of data requires much time and computing resources. In this paper, we propose a parallel FI-growth association rule mining algorithm for rapid extraction of frequent itemsets from large dense databases. We also show that this algorithm can efficiently be parallelized in a cluster computing environment. The preliminary experiments provide quite promising results, with nearly ideal scaling on small clusters and about half of ideal (15 fold speedup) on a thirty-two processor cluster.
INDEX TERMS
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CITATION
null Bundit Manaskasemsak, null Nunnapus Benjamas, Arnon Rungsawang, null Athasit Surarerks, null Putchong Uthayopas, "Parallel association rule mining based on FI-growth algorithm", ICPADS, 2007, Parallel and Distributed Systems, International Conference on, Parallel and Distributed Systems, International Conference on 2007, pp. 1-8, doi:10.1109/ICPADS.2007.4447743
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