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Mining Fuzzy Association Rules Based on Parallel Particle Swarm Optimization Algorithm


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Abstract

The association rule extraction process often involves a large number of candidate item sets and multiple read operations on data sets. With the emergence of massive data, the sequential association rule extraction algorithm also suffers from large I/O overhead and insufficient memory. This paper presents a new multi-swarm parallel multi-mutation particle swarm optimization algorithm (MsP-MmPSO) to search several groups in parallel. Experimental results show that the MsP-MmPSO algorithm has an advantage in terms of execution time over traditional particle swarm optimization, especially when the amount or dimensions of the data increase. Experiments also verify that a good task allocation method can reduce the execution time of the parallel algorithm.


Keywords


Pages

Total Pages: 16
Pages: 147-162

DOI
10.1080/10798587.2014.957482


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Published

Volume: 21
Issue: 2
Year: 2014

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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