A conditionally positive definite kernel function for possibilistic clustering

In the past few years, the kernel-based clustering methods have overpowered the conventional clustering techniques in the field of unsupervised learning due to its strength and effectiveness to deal with nonlinearly separable data and mapping it into higher dimensional feature space by preserving the inner structure of the data. Many kernel functions exist in the literature which works effectively depending on the type of dataset to be used. In this paper, we have proposed a new log kernel function which is embedded in the unsupervised possibilistic clustering and this kernel function is not explored much in research. We have done extensive comparison of the proposed algorithm with few clustering techniques over a test suite of several synthetic and real life datasets. Based on the experimental results, we have proved that our algorithm gives better performance than the previous methods on various comparative parameters like ideal centroids, error rate, misclassification, accuracy and elapsed time.