Impact of distance measures on the performance of clustering algorithms

Abstract

Distance measure plays a vital role in clustering algorithms. Selecting the right distance measure for a given dataset is a challenging problem. In this paper, the effect of six distance measures on three clustering algorithms, K-means, single linkage, and average linkage is investigated. The distance measures include Euclidean, Euclidean squared, Manhattan, Mahalanobis, cosine similarity, and Pearson correlation. We describe all the distance measures pointing out their strengths and weaknesses. The performance of clustering algorithms on distance measures are evaluated on two artificial and four real-life datasets. Experimental results show the impact of distance measures when used for different datasets.