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 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.

DOI: 10.1109/ICPADS.2007.4447743

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@article{Manaskasemsak2007ParallelAR, title={Parallel association rule mining based on FI-growth algorithm}, author={Bundit Manaskasemsak and Nunnapus Benjamas and Arnon Rungsawang and Athasit Surarerks and Putchong Uthayopas}, journal={2007 International Conference on Parallel and Distributed Systems}, year={2007}, volume={2}, pages={1-8} }