Biogeography-Based optimisation for data clustering

Abdelaziz I. Hammouri, Salwani Abdullah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Clustering is an important data analysis and data mining tool that is used in many fields and applications, which aims to find a homogeneous sets of objects based on the degree of similarity and dissimilarity of their attributes. One of the most popular techniques in data clustering is K-means, which is a simple, fast and efficient method that has been applied successfully in many fields. However, K-means has its own drawbacks like highly dependence on the initial solution and can easily trapped into local optima. In this paper, we investigate the behaviour of the newly created meta-heuristic optimisation algorithm called Biogeography-Based Optimisation (BBO) for data clustering with different initial solution generation mechanisms (random initial solution, sequential diversification initial solution, heuristic initial solution) that is based on the idea of migration of species between different habitats. To evaluate the performance of the proposed method, six UCI Machine Learning Repository data sets were used. The performance of the BBO algorithm was compared with well-known data-clustering algorithms that available in the literature, the experimental results showed that the BBO algorithm was able to obtain comparable results.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages951-963
Number of pages13
Volume265
ISBN (Print)9781614994336
DOIs
Publication statusPublished - 2014
Event13th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2014 - Langkawi, Malaysia
Duration: 22 Sep 201424 Sep 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume265
ISSN (Print)09226389

Other

Other13th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2014
CountryMalaysia
CityLangkawi
Period22/9/1424/9/14

Fingerprint

Clustering algorithms
Data mining
Learning systems

Keywords

  • Biogeography-based optimisation
  • Clustering analysis
  • K-means
  • Meta-heuristic

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Hammouri, A. I., & Abdullah, S. (2014). Biogeography-Based optimisation for data clustering. In Frontiers in Artificial Intelligence and Applications (Vol. 265, pp. 951-963). (Frontiers in Artificial Intelligence and Applications; Vol. 265). IOS Press. https://doi.org/10.3233/978-1-61499-434-3-951

Biogeography-Based optimisation for data clustering. / Hammouri, Abdelaziz I.; Abdullah, Salwani.

Frontiers in Artificial Intelligence and Applications. Vol. 265 IOS Press, 2014. p. 951-963 (Frontiers in Artificial Intelligence and Applications; Vol. 265).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hammouri, AI & Abdullah, S 2014, Biogeography-Based optimisation for data clustering. in Frontiers in Artificial Intelligence and Applications. vol. 265, Frontiers in Artificial Intelligence and Applications, vol. 265, IOS Press, pp. 951-963, 13th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2014, Langkawi, Malaysia, 22/9/14. https://doi.org/10.3233/978-1-61499-434-3-951
Hammouri AI, Abdullah S. Biogeography-Based optimisation for data clustering. In Frontiers in Artificial Intelligence and Applications. Vol. 265. IOS Press. 2014. p. 951-963. (Frontiers in Artificial Intelligence and Applications). https://doi.org/10.3233/978-1-61499-434-3-951
Hammouri, Abdelaziz I. ; Abdullah, Salwani. / Biogeography-Based optimisation for data clustering. Frontiers in Artificial Intelligence and Applications. Vol. 265 IOS Press, 2014. pp. 951-963 (Frontiers in Artificial Intelligence and Applications).
@inproceedings{e79b5d08cf6743518af9835fdec36465,
title = "Biogeography-Based optimisation for data clustering",
abstract = "Clustering is an important data analysis and data mining tool that is used in many fields and applications, which aims to find a homogeneous sets of objects based on the degree of similarity and dissimilarity of their attributes. One of the most popular techniques in data clustering is K-means, which is a simple, fast and efficient method that has been applied successfully in many fields. However, K-means has its own drawbacks like highly dependence on the initial solution and can easily trapped into local optima. In this paper, we investigate the behaviour of the newly created meta-heuristic optimisation algorithm called Biogeography-Based Optimisation (BBO) for data clustering with different initial solution generation mechanisms (random initial solution, sequential diversification initial solution, heuristic initial solution) that is based on the idea of migration of species between different habitats. To evaluate the performance of the proposed method, six UCI Machine Learning Repository data sets were used. The performance of the BBO algorithm was compared with well-known data-clustering algorithms that available in the literature, the experimental results showed that the BBO algorithm was able to obtain comparable results.",
keywords = "Biogeography-based optimisation, Clustering analysis, K-means, Meta-heuristic",
author = "Hammouri, {Abdelaziz I.} and Salwani Abdullah",
year = "2014",
doi = "10.3233/978-1-61499-434-3-951",
language = "English",
isbn = "9781614994336",
volume = "265",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "951--963",
booktitle = "Frontiers in Artificial Intelligence and Applications",

}

TY - GEN

T1 - Biogeography-Based optimisation for data clustering

AU - Hammouri, Abdelaziz I.

AU - Abdullah, Salwani

PY - 2014

Y1 - 2014

N2 - Clustering is an important data analysis and data mining tool that is used in many fields and applications, which aims to find a homogeneous sets of objects based on the degree of similarity and dissimilarity of their attributes. One of the most popular techniques in data clustering is K-means, which is a simple, fast and efficient method that has been applied successfully in many fields. However, K-means has its own drawbacks like highly dependence on the initial solution and can easily trapped into local optima. In this paper, we investigate the behaviour of the newly created meta-heuristic optimisation algorithm called Biogeography-Based Optimisation (BBO) for data clustering with different initial solution generation mechanisms (random initial solution, sequential diversification initial solution, heuristic initial solution) that is based on the idea of migration of species between different habitats. To evaluate the performance of the proposed method, six UCI Machine Learning Repository data sets were used. The performance of the BBO algorithm was compared with well-known data-clustering algorithms that available in the literature, the experimental results showed that the BBO algorithm was able to obtain comparable results.

AB - Clustering is an important data analysis and data mining tool that is used in many fields and applications, which aims to find a homogeneous sets of objects based on the degree of similarity and dissimilarity of their attributes. One of the most popular techniques in data clustering is K-means, which is a simple, fast and efficient method that has been applied successfully in many fields. However, K-means has its own drawbacks like highly dependence on the initial solution and can easily trapped into local optima. In this paper, we investigate the behaviour of the newly created meta-heuristic optimisation algorithm called Biogeography-Based Optimisation (BBO) for data clustering with different initial solution generation mechanisms (random initial solution, sequential diversification initial solution, heuristic initial solution) that is based on the idea of migration of species between different habitats. To evaluate the performance of the proposed method, six UCI Machine Learning Repository data sets were used. The performance of the BBO algorithm was compared with well-known data-clustering algorithms that available in the literature, the experimental results showed that the BBO algorithm was able to obtain comparable results.

KW - Biogeography-based optimisation

KW - Clustering analysis

KW - K-means

KW - Meta-heuristic

UR - http://www.scopus.com/inward/record.url?scp=84948764964&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84948764964&partnerID=8YFLogxK

U2 - 10.3233/978-1-61499-434-3-951

DO - 10.3233/978-1-61499-434-3-951

M3 - Conference contribution

AN - SCOPUS:84948764964

SN - 9781614994336

VL - 265

T3 - Frontiers in Artificial Intelligence and Applications

SP - 951

EP - 963

BT - Frontiers in Artificial Intelligence and Applications

PB - IOS Press

ER -