Genetic Weighted K-means for Large-Scale Clustering Problems

Fang-Xiang Wu, Anthony J. Kusalik, and W. J. Zhang, University of Saskatchewan

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and other GA-clustering algorithms without the WKMA operator is demonstrated.


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