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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Amina Bedboudi
University of Badji Mokhtar - Annaba
Computer Science & IT Electrical & Electronics Engineering

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An Heterogeneous Population-Based Genetic Algorithm for Data Clustering Amina Bedboudi; Cherif Bouras; Mohamed Tahar Kimour
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 5, No 3: September 2017
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v5i3.299

Abstract

As a primary data mining method for knowledge discovery, clustering is a technique of classifying a dataset into groups of similar objects. The most popular method for data clustering K-means suffers from the drawbacks of requiring the number of clusters and their initial centers, which should be provided by the user. In the literature, several methods have proposed in a form of k-means variants, genetic algorithms, or combinations between them  for calculating the number of clusters and finding proper clusters centers. However, none of these solutions has provided satisfactory results and determining the number of clusters and the initial centers are still the main challenge in clustering processes. In this paper we present an approach to automatically generate such parameters to achieve optimal clusters using a modified genetic algorithm operating on varied individual structures and using a new crossover operator. Experimental results show that our modified genetic algorithm is a better efficient alternative to the existing approaches.
Co-Authors Cherif Bouras Mohamed Tahar Kimour