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  Improved Version of Kernelized Fuzzy C-Means using Credibility  
  Authors : Prabhjot Kaur
  Cite as:


Fuzzy c-means is a clustering algorithm which performs well with noiseless data-sets. Various disadvantages of FCM are its sensitivity towards noise points and able to detect only spherical clusters due to euclidean distance metric and can work with only linear data. Kernel approaches can improve the performance of conventional clustering. It changes the behavior of algorithm from linear separability to non-linear separability. It can be achieved by using kernel function as a distance metric, which transforms the data to higher dimensional space and find the difference between points considering all the characteristics of data which are not accessible in two dimensional space. Kernel fuzzy C-means (KFCM) algorithm can efficiently work with non-linear data. But still it is sensitive to noisy points. This paper proposed kernel credibilistic fuzzy C-means (KCFCM) algorithm that uses credibility to reduce the sensitivity of noisy points. Several experimental results show that the proposed algorithm can outperform other algorithms for general data with additive noise.


Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 50-54

Figures :02

Tables : 08

Publication Link : Improved Version of Kernelized Fuzzy C-Means using Credibility




Prabhjot Kaur : is working as a Reader in Maharaja Surajmal Institute of Technology. She has done her Ph.D. in Computer Science. She is the member of IEEE, CSI and ISTE.








Fuzzy C-means


Kernel Function

Robust Image Segmentation

This paper proposed a kernel based credibilistic fuzzy Cmeans (KCFCM) algorithm that applies the credibility parameter to the kernel fuzzy C-means (KFCM) algorithm to reduce the noise sensitivity of FCM.We observed empirically that the proposed Kernelized Credibilistic fuzzy C-Means(KCFCM) algorithm gives best results compared to the FCM, CFCM and KFCM algorithms when subjected to image data for general data with additive noise. KCFCM gives appropriate results for brain data it was subjected to and thus we conclude by saying that it is the most appropriate algorithm for image processing and segmentation amongst the algorithms studied. We also would bring to the readers’ notice that the performance of the algorithm is strongly dependent on the value of sigma and the best value of sigma may vary for different datasets.










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