DIMK-means “Distance-based Initialization Method for K-means Clustering Algorithm”
Raed T. Aldahdooh, Wesam M. Ashour · International Journal of Intelligent Systems and Applications · 2013
Partition-based clustering technique is one of several clustering techniques that attempt to directly decompose the dataset into a set of disjoint clusters.Kmeans algorith m 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 d ifferent init ialization approaches were proposed for the K-means algorith m in the last decades.This paper proposes a selection method for init ial cluster centroid in K-means clustering instead of the random selection method.Research provides a detailed performance assessment of the proposed initialization method over many datasets with different dimensions, numbers of observations, groups and clustering complexit ies.Ability to identify the true clusters is the performance evaluation standard in this research.The experimental results show that the proposed initialization method is more effective and converges to mo re accurate clustering results than those of the random initialization method.