Parallel association rule mining based on FI-growth algorithm

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 am...

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Bibliographic Details
Main Authors: Manaskasemsak, B., Benjamas, N., Rungsawang, A., Surarerks, A., Uthayopas, P.
Format: Conference Proceeding
Language:English
Subjects:
Association rules
Clustering algorithms
Concurrent computing
Data engineering
Data mining
Grid computing
Itemsets
Marketing and sales
Partitioning algorithms
Transaction databases
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Summary: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 Fl-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.
ISSN:1521-9097
2690-5965
DOI:10.1109/ICPADS.2007.4447743