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
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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