Genetic algorithm-based clustering ensemble: determination number of clusters
Genetic algorithms (GAs) have been used in the clustering subject. Also, a clustering ensemble as one acceptable clustering method combines the results of multiple clustering methods on a given dataset and creates final clustering on the dataset. In this paper, genetic algorithm base on clustering e...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Inderscience Enterprises Limited
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/6819 |
| _version_ | 1848745186773958656 |
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| author | Mohammadi, M. Azadeh, A. Saberi, Morteza Azaron, A. |
| author_facet | Mohammadi, M. Azadeh, A. Saberi, Morteza Azaron, A. |
| author_sort | Mohammadi, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Genetic algorithms (GAs) have been used in the clustering subject. Also, a clustering ensemble as one acceptable clustering method combines the results of multiple clustering methods on a given dataset and creates final clustering on the dataset. In this paper, genetic algorithm base on clustering ensemble (GACE) is introduced for finding optimal clusters. The most important property of our method is the ability to extract the number of clusters. With this ability, the need for data examination is removed, and then solving related problems will not be time consuming. GACE is applied to eight series of databases. Experimental results were compared with other four clustering methods. Data envelopment analysis (DEA) is used to compare methods. The results of DEA indicate that GACE is the best method. The four methods are co-association function and average link (CAL), co-association function and K-means (CK), hypergraph-partitioning algorithm (HGPA) and cluster-based similarity partitioning (CSPA). |
| first_indexed | 2025-11-14T06:13:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-6819 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:13:22Z |
| publishDate | 2010 |
| publisher | Inderscience Enterprises Limited |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-68192017-02-28T01:30:44Z Genetic algorithm-based clustering ensemble: determination number of clusters Mohammadi, M. Azadeh, A. Saberi, Morteza Azaron, A. DEA CAL co-association function and average link cluster-based similarity partitioning genetic algorithm HGPA GA data envelopment analysis CSPA CK co-association function and K-means hypergraph-partitioning algorithm clustering ensemble Genetic algorithms (GAs) have been used in the clustering subject. Also, a clustering ensemble as one acceptable clustering method combines the results of multiple clustering methods on a given dataset and creates final clustering on the dataset. In this paper, genetic algorithm base on clustering ensemble (GACE) is introduced for finding optimal clusters. The most important property of our method is the ability to extract the number of clusters. With this ability, the need for data examination is removed, and then solving related problems will not be time consuming. GACE is applied to eight series of databases. Experimental results were compared with other four clustering methods. Data envelopment analysis (DEA) is used to compare methods. The results of DEA indicate that GACE is the best method. The four methods are co-association function and average link (CAL), co-association function and K-means (CK), hypergraph-partitioning algorithm (HGPA) and cluster-based similarity partitioning (CSPA). 2010 Journal Article http://hdl.handle.net/20.500.11937/6819 Inderscience Enterprises Limited restricted |
| spellingShingle | DEA CAL co-association function and average link cluster-based similarity partitioning genetic algorithm HGPA GA data envelopment analysis CSPA CK co-association function and K-means hypergraph-partitioning algorithm clustering ensemble Mohammadi, M. Azadeh, A. Saberi, Morteza Azaron, A. Genetic algorithm-based clustering ensemble: determination number of clusters |
| title | Genetic algorithm-based clustering ensemble: determination number of clusters |
| title_full | Genetic algorithm-based clustering ensemble: determination number of clusters |
| title_fullStr | Genetic algorithm-based clustering ensemble: determination number of clusters |
| title_full_unstemmed | Genetic algorithm-based clustering ensemble: determination number of clusters |
| title_short | Genetic algorithm-based clustering ensemble: determination number of clusters |
| title_sort | genetic algorithm-based clustering ensemble: determination number of clusters |
| topic | DEA CAL co-association function and average link cluster-based similarity partitioning genetic algorithm HGPA GA data envelopment analysis CSPA CK co-association function and K-means hypergraph-partitioning algorithm clustering ensemble |
| url | http://hdl.handle.net/20.500.11937/6819 |