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Multiple seeds based evolutionary algorithm for mining Boolean association rules

conference contribution
posted on 2023-05-23, 11:15 authored by Kabir, MMJ, Shuxiang XuShuxiang Xu, Byeong KangByeong Kang, Zhao, Z
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.

History

Publication title

Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining Workshops (PAKDD 2016)

Volume

9794

Editors

H Cao, J Li, R Wang

Pagination

61-72

ISBN

9783319429953

Department/School

School of Information and Communication Technology

Publisher

Springer International Publishing

Place of publication

Switzerland

Event title

Trends and Applications in Knowledge Discovery and Data Mining Workshops (PAKDD 2016)

Event Venue

Auckland, New Zealand

Date of Event (Start Date)

2016-04-19

Date of Event (End Date)

2016-04-19

Rights statement

Copyright 2016 Springer International Publishing

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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