Robust fuzzy clustering techniques for analyzing complicated colon cancer database

Identifying subgroups of genes from the gene expression of microarray high-dimensionality database is useful in discovering subtypes of cancers in Colon cancer database. Using clustering analysis for identifying cancer types in Colon cancer database is an extremely difficult task because of high-dimensionality gene with noise. Most of the existing clustering methods for colon to achieve types of cancers often hamper the interpretability of the structure. Therefore the aim of this paper is to develop suitable clustering techniques based on fuzzy c-means, the typicality of possibilistic c-means approaches, kernel functions, and neighborhood term to identify similar characters of genes and samples for getting cancer subtypes in the colon cancer database. In order to avoid the random selection of initial prototypes of fuzzy clustering based techniques, this paper presents an algorithm to initialize the cluster prototypes. The performance of proposed methods has been evaluated through experimental work on Synthetic dataset, Wine dataset, IRIS dataset, Checkerboard, Time series, and Thyroid dataset. This paper successfully implements the proposed methods in finding subtypes of cancers in Colon cancer database. Compared with the results of recent existed clustering methods on benchmark datasets and Colon cancer database, this paper has shown that the proposed clustering approach can identify more similar objects of the subgroups than the existed methods. The superiority of the proposed methods has been proved through clustering accuracy.