A new Kernelized Fuzzy Possibilistic C-Means for high dimensional data clustering based on kernel-induced distance measure

Data clustering is most commonly used in several clustering applications. Due to the fast development of the internet and its applications, several high dimensional data clustering algorithms has come into existence. It is very complicated to handle high dimensional data clustering by using the traditional clustering algorithms. Hence, in order to overcome this difficulty, a Kernelized Fuzzy Possibilistic C-Means (KFPCM) algorithm has been proposed for effective clustering results. The proposed KFPCM uses a distance measure which is based on the Kernel-Induced Distance Measure. FPCM combines the advantages of both FCM and PCM, moreover the Kernel-Induced Distance measure helps in obtaining better clustering results in case of high dimensional data. The proposed KFPCM is evaluated using the UCI Machine Learning Repository (Iris and Wine dataset) in terms of clustering accuracy and execution time. The results prove the effectiveness of the proposed KFPCM.