KC-Means: A Fast Fuzzy Clustering

Abstract

A novel hybrid clustering method, named KC-Means clustering, is proposed for improving upon the clustering time of the Fuzzy C-Means algorithm. The proposed method combines K-Means and Fuzzy C-Means algorithms into two stages. In the first stage, the K-Means algorithm is applied to the dataset to find the centers of a fixed number of groups. In the second stage, the Fuzzy C-Means algorithm is applied on the centers obtained in the first stage. Comparisons are then made between the proposed and other algorithms in terms of time processing and accuracy. In addition, the mentioned clustering algorithms are applied to a few benchmark datasets in order to verify their performances. Finally, a class of Minkowski distances is used to determine the influence of distance on the clustering performance.

A Systematic Comparative Analysis of Clustering Techniques

“…Atiyah et al [12] proposed a KC-means algorithm by combining two algorithms -K-means and C-means clustering algorithms during two different stages. The K-means algorithm was applied to the dataset at the first stage to find the centres of groups and C-means algorithm was applied during the second stage to the centres obtained previously.…”

“…It is a combination of K-Means and C-Means algorithms [12]. The steps of this algorithm are shown in Fig.…”

“…In the previous section, various clustering techniques with algorithms have been discussed. The present section provides an analysis of various techniques [8], [10]- [12], [15], [16], [19]- [22], [24], [26], [27]. The different methods of clustering discussed above have their own merits and demerits which are shown in Table I.…”