Estimation distribution of algorithm for fuzzy clustering gene expression data

Authors:
Feng Liu;Juan Liu;Jing Feng;Huaibei Zhou
Affiliations:
Computer School of Wuhan University, Wuhan University, Wuhan, China;Computer School of Wuhan University, Wuhan University, Wuhan, China;Computer School of Wuhan University, Wuhan University, Wuhan, China;International School of Software, Wuhan University, Wuhan, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

Citing 3
Cited 1

Data mining: concepts and techniques

Data mining: concepts and techniques
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

A bio inspired estimation of distribution algorithm for global optimization

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the rapid development of genome projects, clustering of gene expression data is a crucial step in analyzing gene function and relationship of conditions. In this paper, we put forward an estimation of distribution algorithm for fuzzy clustering gene expression data, which combines estimation of distribution algorithms and fuzzy logic. Comparing with sGA, our method can avoid many parameters and can converge quickly. Tests on real data show that EDA converges ten times as fast as sGA does in clustering gene expression data. For clustering accuracy, EDA can get a more reasonable result than sGA does in the worst situations although both methods can get the best results in the best situations.