A Study of Normalization Approach on K-Means Clustering Algorithm

Abstract

K-means clustering is a widely used tool for cluster analysis due to its conceptual simplicity and computational efficiency. However, its performance can be distorted when clustering high-dimensional data where the number of variables becomes relatively large and many of them may contain no information about the clustering structure. In this paper, we point out without data normalization, some problems will arise from the many applications of data mining. The effectiveness of the normalization approach on k-means clustering is also demonstrated through a variety of numerical experiments basically z-score, Min-Max and decimal scaling methods. Experimental analysis shows that the z-score performs well and is much better accurate among the three normalization procedures, due to which the number of iterations is reduced by the method.