DIMK-means "Distance-based Initialization Method for K-means Clustering Algorithm"
Partition-based clustering technique is one of several clustering techniques that attempt to directly decompose the dataset into a set of disjoint clusters. K-means algorithm dependence on partition-based clustering technique is popular and widely used and applied to a variety of domains. K-means clustering results are extremely sensitive to the initial centroid; this is one of the major drawbacks of k-means algorithm. Due to such sensitivity; several different initialization approaches were proposed for the K-means algorithm in the last decades.