Knowledge-based genetic algorithm for multidimensional data clustering
In this paper, a new approach of genetic algorithm called knowledge-based Genetic Algorithm (KBGA-Clustering) is proposed for multidimensional data clustering. Basically, this method adopts knowledge of what called as appropriate cluster centre for a fixed number of kcluster. The chromosome which...
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Trans Tech Publications Ltd., Switzerland
2014
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iium-375312017-09-06T04:03:43Z http://irep.iium.edu.my/37531/ Knowledge-based genetic algorithm for multidimensional data clustering Purnomo, Muhammad Ridwan Andi Saleh, Chairul Lagaida, Reny Lituhayu Hassan, Azmi QA76 Computer software T175 Industrial research. Research and development In this paper, a new approach of genetic algorithm called knowledge-based Genetic Algorithm (KBGA-Clustering) is proposed for multidimensional data clustering. Basically, this method adopts knowledge of what called as appropriate cluster centre for a fixed number of kcluster. The chromosome which has inappropriate genes will be penalised with maximum value to prohibit it in the next generation. The experimental result is also provided for KBGA-Clustering and Genetic Algorithm-Clustering (GA-Clustering) to present the performance. Based on the observation, KBGA-Clustering presents better performance and more optimum solution compared to conventional GA-Clustering. Trans Tech Publications Ltd., Switzerland 2014 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37531/1/AMM.606.277.pdf application/pdf en http://irep.iium.edu.my/37531/4/37531_Knowledge-based%20genetic%20algorithm%20for%20multidimensional%20data_SCOPUS.pdf Purnomo, Muhammad Ridwan Andi and Saleh, Chairul and Lagaida, Reny Lituhayu and Hassan, Azmi (2014) Knowledge-based genetic algorithm for multidimensional data clustering. Applied Mechanics and Materials, 606. pp. 277-280. ISSN 1660-9336 http://www.ttp.net/978-3-03835-183-2/5.html 10.4028/www.scientific.net/AMM.606.277 |
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QA76 Computer software T175 Industrial research. Research and development Purnomo, Muhammad Ridwan Andi Saleh, Chairul Lagaida, Reny Lituhayu Hassan, Azmi Knowledge-based genetic algorithm for multidimensional data clustering |
description |
In this paper, a new approach of genetic algorithm called knowledge-based Genetic
Algorithm (KBGA-Clustering) is proposed for multidimensional data clustering. Basically, this
method adopts knowledge of what called as appropriate cluster centre for a fixed number of kcluster.
The chromosome which has inappropriate genes will be penalised with maximum value to
prohibit it in the next generation. The experimental result is also provided for KBGA-Clustering
and Genetic Algorithm-Clustering (GA-Clustering) to present the performance. Based on the
observation, KBGA-Clustering presents better performance and more optimum solution compared
to conventional GA-Clustering. |
format |
Article |
author |
Purnomo, Muhammad Ridwan Andi Saleh, Chairul Lagaida, Reny Lituhayu Hassan, Azmi |
author_facet |
Purnomo, Muhammad Ridwan Andi Saleh, Chairul Lagaida, Reny Lituhayu Hassan, Azmi |
author_sort |
Purnomo, Muhammad Ridwan Andi |
title |
Knowledge-based genetic algorithm for multidimensional data
clustering |
title_short |
Knowledge-based genetic algorithm for multidimensional data
clustering |
title_full |
Knowledge-based genetic algorithm for multidimensional data
clustering |
title_fullStr |
Knowledge-based genetic algorithm for multidimensional data
clustering |
title_full_unstemmed |
Knowledge-based genetic algorithm for multidimensional data
clustering |
title_sort |
knowledge-based genetic algorithm for multidimensional data
clustering |
publisher |
Trans Tech Publications Ltd., Switzerland |
publishDate |
2014 |
url |
http://irep.iium.edu.my/37531/ http://irep.iium.edu.my/37531/ http://irep.iium.edu.my/37531/ http://irep.iium.edu.my/37531/1/AMM.606.277.pdf http://irep.iium.edu.my/37531/4/37531_Knowledge-based%20genetic%20algorithm%20for%20multidimensional%20data_SCOPUS.pdf |
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2023-09-18T20:53:51Z |
last_indexed |
2023-09-18T20:53:51Z |
_version_ |
1777410172339093504 |