Fuzzy cluster centers separation clustering using possibilistic approach

Authors:
Xiaohong Wu;Bin Wu;Jun Sun;Haijun Fu;Jiewen Zhao
Affiliations:
,School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China;Department of Information Engineering, ChuZhou Vocational Technology College, ChuZhou, P.R. China;School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, P.R. China;School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, P.R. China;School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P.R. China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

Citing 7
Cited 0

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Unsupervised possibilistic clustering

Pattern Recognition
A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests

Pattern Recognition Letters
The possibilistic C-means algorithm: insights and recommendations

IEEE Transactions on Fuzzy Systems
Will the real iris data please stand up?

IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Fuzzy c-means (FCM) clustering is based on minimizing the fuzzy within cluster scatter matrix trace but FCM neglects the between cluster scatter matrix trace that controls the distances between the class centroids. Based on the principle of cluster centers separation, fuzzy cluster centers separation (FCCS) clustering is an extended fuzzy c-means (FCM) clustering algorithm. FCCS attaches importance to both the fuzzy within cluster scatter matrix trace and the between cluster scatter matrix trace. However, FCCS has the same probabilistic constraints as FCM, and FCCS is sensitive to noises. To solve this problem, possibilistic cluster centers separation (PCCS) clustering is proposed based on possibilistic c-means (PCM) clustering and FCCS.Experimental results show that PCCS deals with noisy data better than FCCS and has better clustering accuracy than FCM and FCCS.