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This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments. <\/jats:p>","DOI":"10.1142\/s0129065714300083","type":"journal-article","created":{"date-parts":[[2014,1,6]],"date-time":"2014-01-06T03:56:30Z","timestamp":1388980590000},"page":"1430008","source":"Crossref","is-referenced-by-count":62,"title":["A GENETIC GRAPH-BASED APPROACH FOR PARTITIONAL CLUSTERING"],"prefix":"10.1142","volume":"24","author":[{"given":"H\u00c9CTOR D.","family":"MEN\u00c9NDEZ","sequence":"first","affiliation":[{"name":"Computer Science Department, Universidad Aut\u00f3noma de Madrid, 28049, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"DAVID F.","family":"BARRERO","sequence":"additional","affiliation":[{"name":"Departamento de Autom\u00e1tica, Universidad de Alcal\u00e1, 28801, Alcal\u00e1 de Henares, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"DAVID","family":"CAMACHO","sequence":"additional","affiliation":[{"name":"Computer Science Department, Universidad Aut\u00f3noma de Madrid, 28049, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,2,19]]},"reference":[{"key":"rf2","first-page":"1","volume":"39","author":"Dempster A. 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