Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensio...Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensionality cancer medical database clustering techniques have failed to cluster the available subclasses with less error. Therefore this paper presents suitable fuzzy-based clustering techniques to find available subclasses in high-dimensional prostate and breast cancer databases. In addition this paper presents prototype initialization algorithm to avoid random initialization of initial prototypes. In order to evaluate the performance of proposed clustering techniques experimental study has been performed on benchmark databases. Finally the proposed methods have been successfully implemented to find the subclasses of cancers in prostate and breast cancer databases. The clustering results of proposed methods have been validated by evaluating clustering accuracy.展开更多
文摘Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensionality cancer medical database clustering techniques have failed to cluster the available subclasses with less error. Therefore this paper presents suitable fuzzy-based clustering techniques to find available subclasses in high-dimensional prostate and breast cancer databases. In addition this paper presents prototype initialization algorithm to avoid random initialization of initial prototypes. In order to evaluate the performance of proposed clustering techniques experimental study has been performed on benchmark databases. Finally the proposed methods have been successfully implemented to find the subclasses of cancers in prostate and breast cancer databases. The clustering results of proposed methods have been validated by evaluating clustering accuracy.
