Table of Contents
ISRN Artificial Intelligence
Volume 2012, Article ID 929085, 6 pages
Research Article

Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets

1Machine Vision Laboratory, Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran
2Departments of Electrical Engineering and Computer Engineering, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran

Received 13 September 2011; Accepted 18 October 2011

Academic Editor: I. Buciu

Copyright © 2012 Seyed Mohsen Zabihi and Mohammad-R Akbarzadeh-T. 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.


Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy c-means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions FCM (GIFP-FCM) is one of them, which uses ๐ฟ ๐‘ norm distance measure and competitive learning and outperforms the previous algorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes shadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering results by correctly eliminating most outliers during steps of clustering. We name the novel fuzzy clustering method shadowed set-based GIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the efficiency of the proposed method.