WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 11, 2012
Novel Intuitionistic Fuzzy C-Means Clustering for Linearly and Nonlinearly Separable Data
Authors: , ,
Abstract: This paper presents a robust Intuitionistic Fuzzy c-means (IFCM-σ) in the data space and a robust kernel Intutitionistic Fuzzy C-means (KIFCM-σ) algorithm in the high-dimensional feature space with a new distance metric to improve the performance of Intuitionistic Fuzzy C-means (IFCM) which is based upon intuitionistic fuzzy set theory. IFCM considered an uncertainty parameter called hesitation degree and incorporated a new objective function which is based upon intutionistic fuzzy entropy in the conventional Fuzzy C-means. It has shown better performance than conventional Fuzzy C-Means. We tried to further improve the performance of IFCM by incorporating a new distance measure which has also considered the distance variation within a cluster to regularize the distance between a data point and the cluster centroid. Experiments are done using two-dimensional synthetic data-sets, Standard data-sets referred from previous papers. Results have shown that proposed algorithms, especially KIFCM-σ is more effective for linear and nonlinear separation.
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Keywords: Fuzzy Clustering, Intuitionistic Fuzzy C-Means, Robust Clustering, Kernel Intuitionistic fuzzy cmeans, Distance metric, Fuzzy c-means