International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.5, No.7, Jun. 2013

Performance Comparison of Various Robust Data Clustering Algorithms

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Shashank Sharma, Megha Goel, Prabhjot Kaur

Index Terms

Robust Data Algorithms, Fuzzy C Means, Data Clustering, Noiseless Algorithms


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


[1]Bezdek J. C., Pattern Recognition with Fuzzy Pointive Function Algorithm, Plenum, NY, 1981.

[2]Krishnapuram R. and J. Keller, "A Possibilistic Approach to Clustering", IEEE Trans. on Fuzzy Systems, vol .1. No.2,pp. 98-110, 1993.

[3]Pal N.R., K. Pal, J. Keller and J. C. Bezdek ," A Possibilistic Fuzzy c- Means Clustering Algorithm", IEEE Trans. on Fuzzy Systems, vol 13 (4),pp 517-530,2005.

[4]Dave R. N., “Characterization and detection of noise in clustering”, Pattern Rec. Letters, vol. 12(11), pp 657-664, 1991.

[5]Dave R. N., “Robust fuzzy clustering algorithms,” in 2nd IEEE Int. Conf. Fuzzy Systems, San Francisco, CA, Mar. 28-Apr. 1, 1993, pp. 1281-1286.

[6]Chintalapudi K. K. and M. kam, “A noise resistant fuzzy c-means algorithm for clustering,” IEEE conference on Fuzzy Systems Proceedings, vol. 2, May 1998, pp. 1458-1463.

[7]Kaur, P., Gosain, A. (2011), “A Density Oriented Fuzzy C-Means Clustering Algorithm for Recognizing Original Cluster Shapes from Noisy Data” International Journal of Innovative Computing and Applications (IJICA), INDERSCIENCE ENTERPRISES, Vol. 3, No. 2, pp.77–87.

[8]Kaur Prabhjot, Anjana Gosain, “Improving the performance of Fuzzy Clustering Algorithms through Outlier Identification”, 2009 IEEE Conference of Fuzzy sets and Systems, Korea, August 20-24, pp. 373-378.

[9]Kaur Prabhjot, Anjana Gosain, “Density-Oriented Approach to Identify Outliers and Get Noiseless Clusters in Fuzzy C – Means ”, 2010 IEEE Conference of Fuzzy sets and Systems, Korea, Barcelona, Spain.

[10]Rehm F., F. Klawonn, and R. Kruse (2007), “A Novel Approach to Noise Clustering for Outlier Detection”, Applications and Science in Soft Computing, Springer-Verlag 11:489-494.

[11]Dunn, J., 1974. A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybernet. 3, 32–57. 

[12]Bensaid A. M., L.O. hall, J. C. Bezdek, L. P. Clarke, M. L. Silbiger, J. A. Arrington, R. F. Murtagh, “Validity-guided clustering with applications to image segmentation”, IEEE trans. Fuzzy Systems 4 (2) (1996) 112-123.