International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 5, No. 7, 8 Jun. 2013
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Performance Comparison of Various Robust Data Clustering Algorithms
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Author(s)
Shashank Sharma
1,*
Megha Goel
1
Prabhjot Kaur
1
Index Terms
Robust Data Algorithms, Fuzzy C Means, Data Clustering, Noiseless Algorithms
Abstract
Robust clustering techniques are real life clustering techniques for noisy data. They work efficiently in the presence of noise. Fuzzy C-means (FCM) is the first clustering algorithm, based upon fuzzy sets, proposed by J C Bezdek but it does not give accurate results in the presence of noise. In this paper, FCM and various robust clustering algorithms namely: Possibilistic C-Means (PCM), Possibilistic Fuzzy C-means (PFCM), Credibilistic Fuzzy C-means (CFCM), Noise Clustering (NC) and Density Oriented Fuzzy C-Means (DOFCM) are studied and compared based upon robust characteristics of a clustering algorithm. For the performance analysis of these algorithms in noisy environment, they are applied on various noisy synthetic data sets, standard data sets like DUNN data-set, Bensaid data set. In comparison to FCM, PCM, PFCM, CFCM, and NC, DOFCM clustering method identified outliers very well and selected more desirable cluster centroids.
Cite This Paper
Shashank Sharma, Megha Goel, Prabhjot Kaur, "Performance Comparison of Various Robust Data Clustering Algorithms", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.7, pp.63-71, 2013. DOI:10.5815/ijisa.2013.07.09
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