Abstract
A clustering algorithm—Distance Based Gaussian Kernelized Intuitionistic Fuzzy C Means (DBKIFCM) is proposed. This algorithm is based on Gaussian kernel, outlier identification, and intuitionist fuzzy sets. It is intended to resolve the issue of presence of outliers, problem of sensitivity to initialization (STI) and is motivated by good performance of Radial Based Kernelized Intuitionistic Fuzzy C Means (KIFCM-RBF). Experiments are performed on standard 2D data sets such as Diamond (D12 and D15), and Dunn and real-world high dimension data sets such as Fisheriris, Wisconsin breast cancer, and Wine. DBKIFCM outcomes are studied in relation to Fuzzy C Means (FCM), Intuitionistic Fuzzy C Means (IFCM), KIFCM-RBF, Density Oriented Fuzzy C Means (DOFCM). It is observed that proposed approach significantly outperforms the earlier proposed algorithms with respect to outlier identification, effect of noise, issue of STI, and clustering error.
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