A new initialization and performance measure for the rough k-means clustering

Abstract

A new initialization algorithm is proposed in this study to address the issue of random initialization in the rough k-means clustering algorithm refined by Peters. A new means to choose appropriate zeta values in Peters algorithm is proposed. Also, a new performance measure S/O [within-variance (S)/total-variance (O)] index has been introduced for the rough clustering algorithm. The performance criteria such as root-mean-square standard deviation, S/O index, and running time complexity are used to validate the performance of the proposed and random initialization with that of Peters. In addition, other popular initialization algorithms like k-means++, Peters Π, Bradley, and Ioannis are also herein compared. It is found that our proposed initialization algorithm has performed better than the existing initialization algorithms with Peters refined rough k-means clustering algorithm on different datasets with varying zeta values.