K-means clustering using Max-min distance measure

Abstract

The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most popular and widespread partitioning clustering algorithms due to its superior scalability and efficiency. Typically, the K-means algorithm determines the distance between an object and its cluster centroid by Euclidean distance measure. This paper proposes a variant of K-means which uses an alternate distance measure namely, Max-min measure. The modified K-means algorithm is tested with six benchmark datasets taken from UCI machine learning data repository and found that the proposed algorithm takes less number of iterations to converge than the existing one with improved performance.