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Genetic K-means algorithm | IEEE Journals Magazine | IEEE Xplore We define K-means operator, one-step of K-means algorithm, and use it in GKA as a search operator instead of crossover We also define a biased mutation operator specific to clustering called distance-based-mutation
Combining K-Means and a genetic algorithm through a novel arrangement . . . We advance a previous genetic-searching approach called GenClust, with the intervention of fast hill-climbing cycles of K-Means and obtain an algorithm that is faster than its predecessor and achieves clustering results of higher quality
Incremental genetic K-means algorithm and its application in gene . . . In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA) IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA) IGKA outperforms FGKA when the mutation probability is small
A K-means Optimized Clustering Algorithm Based on Improved Genetic . . . To improve the high dependence on the initial clustering center and local optimal solution of K-means algorithm, this paper combines the improved genetic algorithm (GA) with K-means, and proposes an improved K-means algorithm based on genetic algorithm
An Improved Genetic k-means Algorithm for Optimal Clustering An evolutionary technique based on K-Means algorithm for optimal clustering in RN A genetic algorithm-based efficient clustering technique that utilizes the principles of K-Means algorithm is described in this paper
A Prototypes-Embedded Genetic K-means Algorithm In this paper, a genetic algorithm for K-means clustering is proposed The proposed PGKA algorithm can be characterized by the design of its operators, including encoding, crossover, and mutation