A new similarity measure based robust possibilistic c-means clustering algorithm

Authors:
Kexin Jia;Miao He;Ting Cheng
Affiliations:
School of Electronic Engineering, University of Electronic Science and Technology, Chengdu, China;School of Electronic Engineering, University of Electronic Science and Technology, Chengdu, China;School of Electronic Engineering, University of Electronic Science and Technology, Chengdu, China
Venue:
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Year:
2011

Citing 13
Cited 0

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Performance Evaluation of Some Clustering Algorithms and Validity Indices

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Similarity-Based Robust Clustering Method

IEEE Transactions on Pattern Analysis and Machine Intelligence
A cluster validity index for fuzzy clustering

Pattern Recognition Letters
Fuzzy Bayesian validation for cluster analysis of yeast cell-cycle data

Pattern Recognition
A survey of fuzzy clustering algorithms for pattern recognition. I

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validity-guided (re)clustering with applications to image segmentation

IEEE Transactions on Fuzzy Systems
The possibilistic C-means algorithm: insights and recommendations

IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”

IEEE Transactions on Fuzzy Systems
Improved possibilistic C-means clustering algorithms

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

IEEE Transactions on Fuzzy Systems
A Novel Similarity-Based Fuzzy Clustering Algorithm by Integrating PCM and Mountain Method

IEEE Transactions on Fuzzy Systems
A Robust Automatic Merging Possibilistic Clustering Method

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we focus on the development of a new similarity measure based robust possibilistic c-means clustering (RPCM) algorithm which is not sensitive to the selection of initial parameters, robust to noise and outliers, and able to automatically determine the number of clusters. The proposed algorithm is based on an objective function of PCM which can be regarded as special case of similarity based robust clustering algorithms. Several simulations, including artificial and benchmark data sets, are conducted to demonstrate the effectiveness of the proposed algorithm.