Consensus clustering with differential evolution

Miroslav Sabo

Kybernetika (2014)

  • Volume: 50, Issue: 5, page 661-678
  • ISSN: 0023-5954

Abstract

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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.

How to cite

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Sabo, Miroslav. "Consensus clustering with differential evolution." Kybernetika 50.5 (2014): 661-678. <http://eudml.org/doc/262188>.

@article{Sabo2014,
abstract = {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.},
author = {Sabo, Miroslav},
journal = {Kybernetika},
keywords = {consensus clustering; differential evolution; ensemble; data; consensus clustering; differential evolution; ensemble; data},
language = {eng},
number = {5},
pages = {661-678},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Consensus clustering with differential evolution},
url = {http://eudml.org/doc/262188},
volume = {50},
year = {2014},
}

TY - JOUR
AU - Sabo, Miroslav
TI - Consensus clustering with differential evolution
JO - Kybernetika
PY - 2014
PB - Institute of Information Theory and Automation AS CR
VL - 50
IS - 5
SP - 661
EP - 678
AB - 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.
LA - eng
KW - consensus clustering; differential evolution; ensemble; data; consensus clustering; differential evolution; ensemble; data
UR - http://eudml.org/doc/262188
ER -

References

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