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Performance Analysis for Parallel Generalized Association Rule Mining on a Large Scale PC Cluster

By Takahiko Shintani, Masato Oguchi and Masaru Kitsuregawa


One of the most important problems in data mining is discovery of association rules in large database. We had proposed parallel algorithms for mining generalized association rules with classification hierarchy. In this paper, we implemented the proposed algorithms on a large scale PC cluster which consists of one hundred PCs interconnected by an ATM switch, and analyzed the performance of our algorithms using a large amount of transaction dataset. Performance evaluations show our parallel algorithms are effective for handling skew for such large scale parallel systems

Year: 2007
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