A study of normalization approach on k-means clustering algorithm
Year: 2013
Type: article
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.
Authors: U. Dauda, B. Mohammed Ismail
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W1664637288