Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab

Abstract

K-means algorithm is a very popular clustering algorithm which is famous for its simplicity. Distance measure plays a very important rule on the performance of this algorithm. We have different distance measure techniques available. But choosing a proper technique for distance calculation is totally dependent on the type of the data that we are going to cluster. In this paper an experimental study is done in Matlab to cluster the iris and wine data sets with different distance measures and thereby observing the variation of the performances shown.

arXiv comment6 pages, 11 figures, Clustering, K Means

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