Feature Selection and Semisupervised Fuzzy Clustering

Abstract

Abstract Semisupervised fuzzy clustering plays an important role in discovering structure in data set with both labelled and unlabelled data. The proposed method learns the task of classification and feature selection through the generalized form of Fuzzy C-means. Experimental results illustrate appropriate feature selection and classification accuracy with both synthetic and benchmark data sets.