A review: accuracy optimization in clustering ensembles using genetic algorithms
The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as metho...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/18474/ http://psasir.upm.edu.my/id/eprint/18474/1/A%20review.pdf |
| _version_ | 1848843519339266048 |
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| author | Ghaemi, Reza Sulaiman, Md. Nasir Ibrahim, Hamidah Mustapha, Norwati |
| author_facet | Ghaemi, Reza Sulaiman, Md. Nasir Ibrahim, Hamidah Mustapha, Norwati |
| author_sort | Ghaemi, Reza |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple
partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus
clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper
describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research. |
| first_indexed | 2025-11-15T08:16:19Z |
| format | Article |
| id | upm-18474 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:16:19Z |
| publishDate | 2011 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-184742016-09-01T06:53:20Z http://psasir.upm.edu.my/id/eprint/18474/ A review: accuracy optimization in clustering ensembles using genetic algorithms Ghaemi, Reza Sulaiman, Md. Nasir Ibrahim, Hamidah Mustapha, Norwati The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research. Springer 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/18474/1/A%20review.pdf Ghaemi, Reza and Sulaiman, Md. Nasir and Ibrahim, Hamidah and Mustapha, Norwati (2011) A review: accuracy optimization in clustering ensembles using genetic algorithms. Artificial Intelligence Review, 35 (4). pp. 287-318. ISSN 0269-2821; ESSN: 1573-7462 http://link.springer.com/article/10.1007/s10462-010-9195-5?view=classic 10.1007/s10462-010-9195-5 |
| spellingShingle | Ghaemi, Reza Sulaiman, Md. Nasir Ibrahim, Hamidah Mustapha, Norwati A review: accuracy optimization in clustering ensembles using genetic algorithms |
| title | A review: accuracy optimization in clustering ensembles using genetic algorithms |
| title_full | A review: accuracy optimization in clustering ensembles using genetic algorithms |
| title_fullStr | A review: accuracy optimization in clustering ensembles using genetic algorithms |
| title_full_unstemmed | A review: accuracy optimization in clustering ensembles using genetic algorithms |
| title_short | A review: accuracy optimization in clustering ensembles using genetic algorithms |
| title_sort | review: accuracy optimization in clustering ensembles using genetic algorithms |
| url | http://psasir.upm.edu.my/id/eprint/18474/ http://psasir.upm.edu.my/id/eprint/18474/ http://psasir.upm.edu.my/id/eprint/18474/ http://psasir.upm.edu.my/id/eprint/18474/1/A%20review.pdf |