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Multicore Mining of Correlated Patterns

Abstract : In this paper, we present a new approach relevant to the discovery of correlated patterns, based on the use of multicore architectures. Our work rests on a full KDD system and allows one to extract Decision Correlation Rules based on the Chi-squared criterion that include a target column from any database. To achieve this objective, we use a levelwise algorithm as well as contingency vectors, an alternate and more powerful representation of contingency tables, in order to prune the search space. The goal is to parallelize the processing associated with the extraction of relevant rules. The parallelization invokes the PPL (Parallel Patterns Library), which allows a simultaneous access to the whole available cores / processors on modern computers. We finally present first results on the reached performance gains.
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Submitted on : Friday, December 20, 2013 - 5:05:57 PM
Last modification on : Friday, October 22, 2021 - 3:33:26 AM
Long-term archiving on: : Friday, March 21, 2014 - 9:19:05 AM


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  • HAL Id : emse-00921628, version 1


Christian Ernst, Alain Casali. Multicore Mining of Correlated Patterns. IMMM 2013, Nov 2013, Lisboa, Portugal. pp 18-23. ⟨emse-00921628⟩



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