A multi-objective genetic algorithm based fuzzy relational clustering for automatic microarray cancer data clustering

Abstract

In the biological and biomedical research field, Microarray technology has grown into a leading approach. Microarray technology helps to monitor a large number of genes simultaneously based on different experimental conditions. This paper propose a fast elitist non-dominated sorting multi-objective genetic algorithm (NSGA-II) based fuzzy relational clustering approach for clustering microarray cancer expression dataset where it smartly creates a finer trade-off between fuzzy compactness and fuzzy separation of the clusters. Binary encoding schema is used for chromosomes while encoding the multi-objective GA. In this case it encodes the variable length numbers of clusters. Fuzzy Relational Clustering method is used to automatically assign the membership to all data point for each cluster and it produce a clusters for the given microarray cancer dataset. In the multi-objective genetic algorithm, a set of non-dominated solutions is given with optimizing objectives without dominating to any other solution. The simulation results exhibits that the intended method gives promising results in the complex, overlapped, high-dimensional microarray cancer datasets.