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Parallel Genetic Algorithm Model to Extract Association Rules

Authors:
Ahmad Taleb
Anwar Yahya
Nasser Taleb

Keywords: Data mining, FP-Growth, Multi-objective evolutionary algorithms, scalability, performance, Parallel GA

Abstract:
Over the past generation, the process of discovering interesting association rules in data mining and knowledge discovery has become a cornerstone of contemporary decision support environments. While most of the existing algorithms do indeed focus on discovering high interestingness and accuracy relationships between items in the databases, they tend to have limited scalability and performance. In this paper, we discuss a Parallel Genetic Algorithm Model (PGAM) that has been designed as a scalable and high performance association rules engine. Experimental results demonstrate that the model offers the potential to optimize both scalability and performance in association rules mining.

Pages: 56 to 64

Copyright: Copyright (c) IARIA, 2013

Publication date: January 27, 2013

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-247-9

Location: Seville, Spain

Dates: from January 27, 2013 to February 1, 2013