Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved algorithm for clustering gene expression data
Bioinformatics
Analysis of Biological Data: A Soft Computing Approach - Vol. 3
Analysis of Biological Data: A Soft Computing Approach - Vol. 3
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
A New Convergence Proof of Fuzzy c-Means
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
In this article, a novel multiobjective variable string length real coded genetic fuzzy clustering scheme for clustering microarray gene expression data has been proposed. The proposed technique automatically evolves the number of clusters along with the clustering result. The multiobjective variable string length clustering technique encodes the cluster centers in its chromosomes and simultaneously optimizes two fuzzy validity indices namely PBM index and Xie-Beni validity measure. In the final generation, it produces a set of nondominated solutions, from which the best solution is selected using Silhouette index which is independent of the number of clusters. The corresponding chromosome length provides the number of clusters. The proposed method is applied on three publicly available real life gene expression data. Superiority of the proposed method over some other well known clustering algorithms has been demonstrated quantitatively.