On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering
In a previous work, suitable initialisation techniques were incorporated with semi-supervised Fuzzy c-Means clustering (ssFCM) to improve clustering results on a trial and error basis. In this work, we present a single fully-automatic version of an existing semi-supervised Fuzzy c-means clustering f...
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| Format: | Article |
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Springer
2016
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| Online Access: | https://eprints.nottingham.ac.uk/38397/ |
| _version_ | 1848795602221006848 |
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| author | Lai, Daphne Teck Ching Garibaldi, Jonathan M. |
| author_facet | Lai, Daphne Teck Ching Garibaldi, Jonathan M. |
| author_sort | Lai, Daphne Teck Ching |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In a previous work, suitable initialisation techniques were incorporated with semi-supervised Fuzzy c-Means clustering (ssFCM) to improve clustering results on a trial and error basis. In this work, we present a single fully-automatic version of an existing semi-supervised Fuzzy c-means clustering framework which uses genetically-modified prototypes (ssFCMGA). Initial prototypes are generated by GA to initialise the ssFCM algorithm without experimentation of different initialisation techniques. The framework is tested on a real, biomedical dataset NTBC and on the Arrhythmia UCI dataset, using varying amounts of labelled data from 10% to 60% of the total data patterns. Different ssFCM threshold values and fitness functions for ssFCMGA are also investigated (sGAs). We used accuracy and NMI to measure class-label agreement and internal measures WSS, BSS, CH, CWB, DB and DU to evaluate cluster quality of the clustering algorithms. Results are compared with those produced by the existing ssFCM. While ssFCMGA and sGAs produced slightly lower agreement level than ssFCM with known class labels based on accuracy and NMI, the other six measurements showed improvement in the results in terms of compactness and well-separatedness (cluster quality), particularly when labelled data are low at 10%. Furthermore, the cluster quality are shown to further improve using ssFCMGA with a more complex fitness function (sGA2). This demonstrates the application of GA in ssFCM improves cluster quality without exploration of different initialisation techniques. |
| first_indexed | 2025-11-14T19:34:42Z |
| format | Article |
| id | nottingham-38397 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:34:42Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-383972020-05-04T18:16:08Z https://eprints.nottingham.ac.uk/38397/ On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering Lai, Daphne Teck Ching Garibaldi, Jonathan M. In a previous work, suitable initialisation techniques were incorporated with semi-supervised Fuzzy c-Means clustering (ssFCM) to improve clustering results on a trial and error basis. In this work, we present a single fully-automatic version of an existing semi-supervised Fuzzy c-means clustering framework which uses genetically-modified prototypes (ssFCMGA). Initial prototypes are generated by GA to initialise the ssFCM algorithm without experimentation of different initialisation techniques. The framework is tested on a real, biomedical dataset NTBC and on the Arrhythmia UCI dataset, using varying amounts of labelled data from 10% to 60% of the total data patterns. Different ssFCM threshold values and fitness functions for ssFCMGA are also investigated (sGAs). We used accuracy and NMI to measure class-label agreement and internal measures WSS, BSS, CH, CWB, DB and DU to evaluate cluster quality of the clustering algorithms. Results are compared with those produced by the existing ssFCM. While ssFCMGA and sGAs produced slightly lower agreement level than ssFCM with known class labels based on accuracy and NMI, the other six measurements showed improvement in the results in terms of compactness and well-separatedness (cluster quality), particularly when labelled data are low at 10%. Furthermore, the cluster quality are shown to further improve using ssFCMGA with a more complex fitness function (sGA2). This demonstrates the application of GA in ssFCM improves cluster quality without exploration of different initialisation techniques. Springer 2016-10-21 Article PeerReviewed Lai, Daphne Teck Ching and Garibaldi, Jonathan M. (2016) On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering. Advances in Intelligent Systems and Computing, 532 . pp. 3-14. ISSN 2194-5357 semi-supervised genetic algorithms fuzzy clustering http://www.springer.com/gp/book/9783319485164 doi:10.1007/978-3-319-48517-1_1 doi:10.1007/978-3-319-48517-1_1 |
| spellingShingle | semi-supervised genetic algorithms fuzzy clustering Lai, Daphne Teck Ching Garibaldi, Jonathan M. On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering |
| title | On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering |
| title_full | On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering |
| title_fullStr | On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering |
| title_full_unstemmed | On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering |
| title_short | On using genetic algorithm for initialising semi-supervised fuzzy c-means clustering |
| title_sort | on using genetic algorithm for initialising semi-supervised fuzzy c-means clustering |
| topic | semi-supervised genetic algorithms fuzzy clustering |
| url | https://eprints.nottingham.ac.uk/38397/ https://eprints.nottingham.ac.uk/38397/ https://eprints.nottingham.ac.uk/38397/ |