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Multiple seeds based evolutionary algorithm for mining Boolean association rules


Kabir, MMJ and Xu, S and Kang, BH and Zhao, Z, Multiple seeds based evolutionary algorithm for mining Boolean association rules, Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining Workshops (PAKDD 2016), 19 April 2016, Auckland, New Zealand, pp. 61-72. ISBN 9783319429953 (2016) [Refereed Conference Paper]

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Copyright 2016 Springer International Publishing

DOI: doi:10.1007/978-3-319-42996-0_6


Most of the association rule mining algorithms use a single seed for initializing a population without paying attention to the effectiveness of an initial population in an evolutionary learning. Recently, researchers show that an initial population has significant effects on producing good solutions over several generations of a genetic algorithm. There are two significant challenges raised by single seed based genetic algorithms for real world applications: (1) solutions of a genetic algorithm are varied, since different seeds generate different initial populations, (2) it is a hard process to define an effective seed for a specific application. To avoid these problems, in this paper we propose a new multiple seeds based genetic algorithm (MSGA) which generates multiple seeds from different domains of a solution space to discover high quality rules from a large data set. This approach introduces m-domain model and m-seeds selection process through which the whole solution space is subdivided into m-number of same size domains and from each domain it selects a seed. By using these seeds, this method generates an effective initial population to perform an evolutionary learning of the fitness value of each rule. As a result, this method obtains strong searching efficiency at the beginning of the evolution and achieves fast convergence along with the evolution. MSGA is tested with different mutation and crossover operators for mining interesting Boolean association rules from different real world data sets and compared the results with different single seeds based genetic algorithms.

Item Details

Item Type:Refereed Conference Paper
Keywords:multiple seeds, genetic algorithm, Boolean association rules
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Kabir, MMJ (Mr Mir Kabir)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Kang, BH (Professor Byeong Kang)
UTAS Author:Zhao, Z (Dr Zongyuan Zhao)
ID Code:110175
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2016-07-18
Last Modified:2018-02-28

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