The K-means method is one of the most widely used clustering methods and has been implemented\nin many fields of science and technology. One of the major problems of the k-means algorithm is that\nit may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are\nadaptive heuristic search algorithm based on the evolutionary principles of natural selection and\ngenetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently\neliminates this empty cluster problem. Results of simulation experiments using several data sets\nprove our claim.
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