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

Finding subtypes of cancer in breast cancer database is an extremely dificult task because ofheavy noise by measurement error.Most of the recent clustering techniques for breast cancerdatabase to achieve cancerous and noncancerous often weigh down the interpretability of thestructure.Hence,this paper tries to find effective Fuzzy C-Means-based clustering techniques toidentify the proper subtypes of cancer in breast cancer database,This paper obtains the objectivefunction of ffective Fuzzy C-Means clustering techriques by incorporating the kermel induceddist ance function,Renyi's entropy function,weighted dist ance measure and neighborhood ternsbased spatial context.The efectiveness of the proposed methods are proved through the ex-perimental works on Lung cancer database,IRIS dataset,Wine dat aset,Checkerboard dataset,Time Series dataset and Yeast dataset.Finlly,the proposed methods are implemented suc-cesfully to cluster the breast cancer dat abase into cancerous and noncancerous.The clusteringaccuracy has been validat ed through error matrix and silhouette method.