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DKFCM: Kernelized Approach to Density-Oriented Clustering

  • Anjana GosainEmail author
  • Tusharika Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

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.

Keywords

Fuzzy clustering FCM KFCM Density-oriented approach Distance metric 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University School of Information and Communication Technology, Guru Gobind Singh Indraprastha UniversityDwarka, New DelhiIndia

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