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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.<\/jats:p>","DOI":"10.1515\/jisys-2018-0063","type":"journal-article","created":{"date-parts":[[2018,4,25]],"date-time":"2018-04-25T18:16:23Z","timestamp":1524680183000},"page":"529-539","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Genetic Fuzzy c-Means"],"prefix":"10.1515","volume":"29","author":[{"given":"Khalid","family":"Jebari","sequence":"first","affiliation":[{"name":"Technologies and Sciences Faculty Tangier, Department of Computer Sciences , Tangier , Morocco"}]},{"given":"Abdelaziz","family":"Elmoujahid","sequence":"additional","affiliation":[{"name":"LCS Laboratory, Faculty of Sciences, Department of Physics, Mohamed V University , Rabat , Morocco"}]},{"given":"Aziz","family":"Ettouhami","sequence":"additional","affiliation":[{"name":"LCS Laboratory, Faculty of Sciences , Department of Physics, Mohamed V University , Rabat , Morocco"}]}],"member":"374","published-online":{"date-parts":[[2018,4,25]]},"reference":[{"key":"2025120523362722509_j_jisys-2018-0063_ref_001","doi-asserted-by":"crossref","unstructured":"A. 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