Efficiency analysis of kernel functions in uncertainty based c-means algorithms

Application of clustering algorithms for investigating real life data has concerned many researchers and vague approaches or their hybridization with other analogous approaches has gained special attention due to their great effectiveness. Recently, rough intuitionistic fuzzy c-means algorithm has been proposed by Tripathy et al [3] and they established its supremacy over all other algorithms contained in the same set. Replacing the Euclidean distance metric with kernel induced metric makes it possible to cluster the objects which are linearly inseparable in the original space. In this paper a comparative analysis is performed over the Gaussian, hyper tangent and radial basis kernel functions by their application on various vague clustering approaches like rough c-means (RCM), intuitionistic fuzzy c-means (IFCM), rough fuzzy c-means (RFCM) and rough intuitionistic fuzzy c-means (RIFCM). All clustering algorithms have been tested on synthetic, user knowledge modeling and human activity recognition datasets taken from UCI repository against the standard accuracy indexes for clustering. The results reveal that for small sized datasets Gaussian kernel produces more accurate clustering than radial basis and hyper tangent kernel functions however for the datasets which are considerably large hyper tangent kernel is superior to other kernel functions. All experiments have been carried out using C language and python libraries have been used for statistical plotting.