A Comparative Study on Data Mining Algorithm for Gene Cancer Analysis

Abstract: DNA microarray data now permit scientists to screen thousand of genes simultaneously and determine whether those genes are active or silent in normal and cancerous tissues. With the advancement of microarray technology, new analytical methods must be developed to find out whether microarray data have discriminative signatures of gene expression over normal or cancerous tissues. Fuzzy C-Means is a method of clustering which allows one piece of data to belong to two or many clusters. This method is frequently used in pattern recognition. It is based on minimizing functions. Fuzzy Partitioning is carried out through an interactive optimization of the objective function, with the update of membership the cluster centers. Fuzzy c-means is one of them and it is used widely in such applications as a clustering algorithm. In this study, we applied a different clustering algorithm, an artificial immune system (AIS), for data reduction process. We realized the performance evaluation experiments on standard Chain link and Iris datasets, while the main application was conducted by using Wisconsin Breast Cancer dataset and Pima Indians dataset which were taken from the UCI Machine learning repository. Keywords: K-mean, Fuzzy C-Means, Microarray, Gene selection, Classification.