FGKA: a Fast Genetic K-means Clustering Algorithm. In this paper, we propose a new clustering algorithm called Fast Genetic K-means Algorithm (FGKA). FGKA is inspired by the Genetic K-means Algorithm (GKA) proposed by Krishna and Murty in 1999 but features several improvements over GKA. Our experiments indicate that, while K-means algorithm might converge to a local optimum, both FGKA and GKA always converge to the global optimum eventually but FGKA runs much faster than GKA.

References in zbMATH (referenced in 6 articles )

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  1. Kazakovtsev, Lev; Rozhnov, Ivan; Shkaberina, Guzel; Orlov, Viktor: (k)-means genetic algorithms with greedy genetic operators (2020)
  2. D’Urso, Pierpaolo; Massari, Riccardo: Fuzzy clustering of mixed data (2019)
  3. Tsai, Chun-Wei; Huang, Ko-Wei; Yang, Chu-Sing; Chiang, Ming-Chao: A fast particle swarm optimization for clustering (2015) ioport
  4. Chiang, Ming-Chao; Tsai, Chun-Wei; Yang, Chu-Sing: A time-efficient pattern reduction algorithm for (k)-means clustering (2011) ioport
  5. Chao, Yi-Hsiang; Tsai, Wei-Ho; Wang, Hsin-Min; Chang, Ruei-Chuan: Improving the characterization of the alternative hypothesis via minimum verification error training with applications to speaker verification (2009)
  6. Özyer, Tansel; Alhajj, Reda: Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer (2009) ioport