Clustering DNA microarray data via multi-objective genetic algorithm

DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data. A first step toward addressing this challenge is the use of clustering data. Cluster analysis seeks to partition given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups.Clustering algorithms in general need the number of clusters as a priori, which is mostly hard for domain expert to estimate. In this thesis, in order to overcome this problem, a multi-objective genetic algorithm based method is proposed. The method combines the K-means clustering algorithm with multi-objective genetic algorithm process. The experimental results conducted on Leukemia, Lymphoma and Colon cancer databases have been compared to Dunn, Davies Bouldien, Silhoutte, C, SD ve S-Dbw cluster validation indexes which are widely used in the literature. So, we demonstrate the applicability and effectiveness of the proposed clustering approach.