Automatic Genetic Fuzzy c-Means
| Autor: | Jebari Khalid, Elmoujahid Abdelaziz, Ettouhami Aziz |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Journal of Intelligent Systems, Vol 29, Iss 1, Pp 529-539 (2018) |
| Druh dokumentu: | article |
| ISSN: | 0334-1860 2191-026X 2018-0063 |
| DOI: | 10.1515/jisys-2018-0063 |
| Popis: | Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choices have a direct impact on the clustering outcome. This paper presents an improved algorithm called automatic genetic fuzzy c-means that evolves the number of clusters and provides the initial centroids. The proposed algorithm uses a genetic algorithm with a new crossover operator, a new mutation operator, and modified tournament selection; further, it defines a new fitness function based on three cluster validity indices. Real data sets are used to demonstrate the effectiveness, in terms of quality, of the proposed algorithm. |
| Databáze: | Directory of Open Access Journals |
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