FUZZY C-MEANS IN FINDING SUBTYPES OF CANCERS IN CANCER DATABASE

Abstract

Finding subtypes of cancer in breast cancer database is an extremely difficult task because of heavy noise by measurement error. Most of the recent clustering techniques for breast cancer database to achieve cancerous and noncancerous often weigh down the interpretability of the structure. Hence, this paper tries to find effective Fuzzy C-Means-based clustering techniques to identify the proper subtypes of cancer in breast cancer database. This paper obtains the objective function of effective Fuzzy C-Means clustering techniques by incorporating the kernel induced distance function, Renyi's entropy function, weighted distance measure and neighborhood terms-based spatial context. The effectiveness of the proposed methods are proved through the experimental works on Lung cancer database, IRIS dataset, Wine dataset, Checkerboard dataset, Time Series dataset and Yeast dataset. Finally, the proposed methods are implemented successfully to cluster the breast cancer database into cancerous and noncancerous. The clustering accuracy has been validated through error matrix and silhouette method.