The challenge in medical breast cancer database is to differentiate the sub types of cancers in the data. Analyzing the medical breast cancer database is most important one in identifying cancer types which cause deaths. Therefore in order to analyze the types of diseases in cancer databases this paper develops fuzzy set based unsupervised effective clustering technique and implements it with breast cancer database to divide it into available subtypes. This paper introduces the objective function of unsupervised effective proposed clustering technique by incorporating kernel induced distance, kernel functions, and possibilistic memberships. Through the experimental part of this paper the efficiency of proposed method is proved.

Clustering, Fuzzy C-Means, Kernel Distance, Breast Cancer Data

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