Performance Comparison of Various Robust Data Clustering Algorithms
Автор: Shashank Sharma, Megha Goel, Prabhjot Kaur
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 7 vol.5, 2013 года.
Бесплатный доступ
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.
Robust Data Algorithms, Fuzzy C Means, Data Clustering, Noiseless Algorithms
Короткий адрес: https://sciup.org/15010445
IDR: 15010445
Список литературы Performance Comparison of Various Robust Data Clustering Algorithms
- Bezdek J. C., Pattern Recognition with Fuzzy Pointive Function Algorithm, Plenum, NY, 1981.
- Krishnapuram R. and J. Keller, "A Possibilistic Approach to Clustering", IEEE Trans. on Fuzzy Systems, vol .1. No.2,pp. 98-110, 1993.
- 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.
- Dave R. N., “Characterization and detection of noise in clustering”, Pattern Rec. Letters, vol. 12(11), pp 657-664, 1991.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Dunn, J., 1974. A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybernet. 3, 32–57.
- 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.
