Geodesic
Consensus clustering with differential evolution
Kybernetika, Tome 50 (2014) no. 5, pp. 661-678.

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Consensus clustering algorithms are used to improve properties of traditional clustering methods, especially their accuracy and robustness. In this article, we introduce our approach that is based on a refinement of the set of initial partitions and uses differential evolution algorithm in order to find the most valid solution. Properties of the algorithm are demonstrated on four benchmark datasets.
DOI : 10.14736/kyb-2014-5-0661
Classification : 62H30, 92G30
Keywords: consensus clustering; differential evolution; ensemble; data 
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     author = {Sabo, Miroslav},
     title = {Consensus clustering with differential evolution},
     journal = {Kybernetika},
     pages = {661--678},
     publisher = {mathdoc},
     volume = {50},
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     mrnumber = {3301853},
     zbl = {1308.62132},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2014-5-0661/}
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Sabo, Miroslav. Consensus clustering with differential evolution. Kybernetika, Tome 50 (2014) no. 5, pp. 661-678. doi : 10.14736/kyb-2014-5-0661. http://geodesic.mathdoc.fr/articles/10.14736/kyb-2014-5-0661/

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