Frequent itemset mining (FIM) algorithms extract subsets of items that occurs frequently in a collection of sets. FIM is a key analysis in several data mining applications, and the FIM tools are among the most computationally intensive data mining ones. In this work we present a many-core parallel version of a state-of-the-art FIM algorithm, DCI, whose sequential version resulted, for most of the tested datasets, better than FP-Growth, one of the most efficient algorithms for FIM. We propose a couple of parallelization strategies for Graphics Processing Units (GPU) suitable for different resource availability, and we present the results of several experiments conducted on real-world and synthetic datasets. © 2012 IEEE.

gpuDCI: Exploiting GPUs in Frequent Itemset Mining

SILVESTRI, Claudio;ORLANDO, Salvatore
2012-01-01

Abstract

Frequent itemset mining (FIM) algorithms extract subsets of items that occurs frequently in a collection of sets. FIM is a key analysis in several data mining applications, and the FIM tools are among the most computationally intensive data mining ones. In this work we present a many-core parallel version of a state-of-the-art FIM algorithm, DCI, whose sequential version resulted, for most of the tested datasets, better than FP-Growth, one of the most efficient algorithms for FIM. We propose a couple of parallelization strategies for Graphics Processing Units (GPU) suitable for different resource availability, and we present the results of several experiments conducted on real-world and synthetic datasets. © 2012 IEEE.
2012
20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2012)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/28572
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