Abstract
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.
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Gosain, A., Singh, T. (2019). DKFCM: Kernelized Approach to Density-Oriented Clustering. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_29
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DOI: https://doi.org/10.1007/978-981-10-8055-5_29
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