A new approach for measuring the validity of the fuzzy c-means algorithm

Authors:
George E. Tsekouras;Haralambos Sarimveis
Affiliations:
Laboratory of Multimedia Applications, Department of Cultural Technology and Communication, University of the Aegean, Faonos and Harilaou Trikoupi Str., GR-81100 Mytilene, Greece;National Technical University of Athens, School of Chemical Engineering, 9, Heroon Polytechniou Str., Zografou Campus, Athens 15780, Greece
Venue:
Advances in Engineering Software
Year:
2004

Citing 9
Cited 10

Convergence theory for fuzzy c-means: counterexamples and repairs

IEEE Transactions on Systems, Man and Cybernetics
Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed representation of fuzzy rules and its application to pattern classification

Fuzzy Sets and Systems
Fast fuzzy clustering

Fuzzy Sets and Systems
Unsupervised fuzzy clustering

Pattern Recognition Letters
Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules

Pattern Recognition Letters
Validity-guided (re)clustering with applications to image segmentation

IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model

IEEE Transactions on Fuzzy Systems

On fuzzy cluster validity indices

Fuzzy Sets and Systems
Prediction of neonatal jaundice using fuzzy clustering methods

BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Using fuzzy clustering methods for delineating urban housing submarkets

Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Robust cluster validity indexes

Pattern Recognition
A selection model for optimal fuzzy clustering algorithm and number of clusters based on competitive comprehensive fuzzy evaluation

IEEE Transactions on Fuzzy Systems
Applications of an enhanced cluster validity index method based on the Fuzzy C-means and rough set theories to partition and classification

Expert Systems with Applications: An International Journal
A validity criterion for fuzzy clustering

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
An enhanced classification method comprising a genetic algorithm, rough set theory and a modified PBMF-index function

Applied Soft Computing
Improved-FCM-Based readout segmentation and PRML detection for photochromic optical disks

FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
A robust adaptive clustering analysis method for automatic identification of clusters

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper an index to validate the fuzzy c-means algorithm is developed. The proposed index adopts a compactness measure to describe the variation of clusters, and introduces the fuzzy separation concept to determine the isolation of clusters. The basic design element of fuzzy separation is the fuzzy deviation between two cluster centers, which is calculated by taking into account the locations of the rest of the centers. Limiting analysis indicates the sensitivity of the index with respect to the design parameters, while the application to two data sets illustrates the effectiveness of the index in detecting the correct fuzzy c-partitions.