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Advances in Fuzzy Systems
Volume 2018, Article ID 2634861, 8 pages
https://doi.org/10.1155/2018/2634861
Research Article

-Means: A Fast Fuzzy Clustering

1Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
2School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

Correspondence should be addressed to Adel Mohammadpour; ri.ca.tua@leda

Received 17 January 2018; Accepted 17 April 2018; Published 3 June 2018

Academic Editor: Ferdinando Di Martino

Copyright © 2018 Israa Abdzaid Atiyah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A novel hybrid clustering method, named -Means clustering, is proposed for improving upon the clustering time of the Fuzzy -Means algorithm. The proposed method combines -Means and Fuzzy -Means algorithms into two stages. In the first stage, the -Means algorithm is applied to the dataset to find the centers of a fixed number of groups. In the second stage, the Fuzzy -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.