Due to uncertainty value of objects in microarray gene expression high dimensional cancer database, finding available subtypes of cancers is considered as challenging task. Researchers have invented mathematical assisted clustering techniques in clustering relevant gene expression of cancer subtypes, but the techniques have failed to provide proper outcome results with less error. Hence, it is an essential one in finding efficient computational clustering techniques to cluster the high dimensional gene expression cancer database for perfect diagnosis of cancer subtypes. This paper presents robust clustering techniques to identify perfect similarity between the uncertain objects of high dimensional cancer database. In order to obtain the robust clustering techniques, this paper incorporates both membership functions of fuzzy c-means and possibilistic c-means. In addition, this paper presents prototype initialization algorithm to avoid random initialization of initial prototypes. Benchmarks datasets were used to show the effectiveness of the proposed methods. The proposed methods were successfully implemented with microarray high dimensional gene expression cancer databases to separate available subtypes of cancer regions. The clustering accuracies of proposed and existed clustering methods indicate that the proposed methods are superior to the existed methods. 
Robust fuzzy clustering algorithms in analyzing high-dimensional cancer databases