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

Authors:
Sung-Bae Cho;Si-Ho Yoo
Affiliations:
Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, South Korea;Department of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, South Korea
Venue:
Pattern Recognition
Year:
2006

Citing 7
Cited 4

A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new cluster validity index for the fuzzy c-mean

Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Cluster validation techniques for genome expression data

Signal Processing - Special issue: Genomic signal processing
Fuzzy cluster validation index based on inter-cluster proximity

Pattern Recognition Letters
A cluster validity index for fuzzy clustering

Pattern Recognition Letters
On cluster validity for the fuzzy c-means model

IEEE Transactions on Fuzzy Systems

On fuzzy cluster validity indices

Fuzzy Sets and Systems
A selection model for optimal fuzzy clustering algorithm and number of clusters based on competitive comprehensive fuzzy evaluation

IEEE Transactions on Fuzzy Systems
A validity criterion for fuzzy clustering

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
A new similarity measure based robust possibilistic c-means clustering algorithm

WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II

Quantified Score

Hi-index	0.01

Visualization

Abstract

Clustering for the analysis of the genes organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster and analyzing the functions of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to only one group. In this paper, a Bayesian-like validation method selecting a fuzzy partition is proposed to evaluate the fuzzy partitions effectively. The theoretical interpretation of the obtained memberships is beyond the scope of this paper, and an empirical evaluation of the proposed method is conducted by comparing to the four representative conventional fuzzy cluster validity measures in four well-known datasets. Analysis of yeast cell-cycle data follows to evaluate the proposed method.