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Kernel Based Rough-Fuzzy C-Means

  • Rohan Bhargava
  • Balakrushna Tripathy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

Data clustering has found its usefulness in various fields. Algorithms are mostly developed using euclidean distance. But it has several drawbacks which maybe rectified by using kernel distance formula. In this paper, we propose a kernel based rough-fuzzy C-Means (KRFCM) algorithm and use modified version of the performance indexes (DB and D) obtained by replacing the distance function with kernel function. We provide a comparative analysis of RFCM with KRFCM by computing their DB and D index values. The analysis is based upon both numerical as well as image datasets. The results establish that the proposed algotihtm outperforms the existing one.

Keywords

Clustering Kernel DB Index Dunn Index Rough-Fuzzy C-Means 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rohan Bhargava
    • 1
  • Balakrushna Tripathy
    • 1
  1. 1.School of Computing Science and EngineeringVIT UniversityVelloreIndia

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