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The K-means segmentation clustering algorithm was first developed in 1967 by MacQueen [32]. The purpose of the algorithm is to divide the data into K clusters. Each cluster is presented with a center of gravity named centroid. The K value is determined by the user. An iterative method is used to divide the data into clusters. The distance function obtains the point to which each data point will be assigned.