DKFCM: Kernelized Approach to Density-Oriented Clustering
In this chapter, we have proposed a new clustering algorithm: density-oriented kernel-based FCM (DKFCM). It uses kernelized approach for clustering after identifying outliers using density-oriented approach. We have used two types of kernel functions for the implementation of DKFCM—Gaussian function and RBF function—and compared its result with other fuzzy clustering algorithms such as fuzzy C-means (FCM), kernel fuzzy C-means (KFCM), and density-oriented fuzzy C-means (DOFCM) to show the effectiveness of the proposed algorithm. We have demonstrated the experimental performance of these algorithms on two standard datasets: DUNN and D15.
KeywordsFuzzy clustering FCM KFCM Density-oriented approach Distance metric
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