Robust interval competitive agglomeration clustering algorithm with outliers

International Journal of Fuzzy Systems

Volumn 12, Issue 3, 2010, Pages 227-236

  Jeng, Jin Tsong ^a   Chuang, Chen Chia ^b   Tseng, Chih Cheng ^b   Juan, Chang Jung ^c  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL ILAN UNIVERSITY   (Taiwan)

^c HWA HSIA INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Interval Fuzzy c means clustering algorithm and Outliers; Robust Interval Competitive Agglomeration Clustering Algorithm; Symbolic interval values data

Indexed keywords

AGGLOMERATION; FUZZY CLUSTERING; STATISTICS;

C MEAN CLUSTERING ALGORITHMS; COMPETITIVE AGGLOMERATION CLUSTERING ALGORITHM; FUZZY C-MEAN CLUSTERING ALGORITHM; FUZZY C-MEANS CLUSTERING; FUZZY C-MEANS CLUSTERING ALGORITHMS; INTERVAL FUZZY C-MEAN CLUSTERING ALGORITHM AND OUTLIER; NUMBER OF CLUSTERS; ROBUST CLUSTERING; ROBUST INTERVAL COMPETITIVE AGGLOMERATION CLUSTERING ALGORITHM; SYMBOLIC INTERVAL-VALUES DATA;

CLUSTERING ALGORITHMS;

EID: 77957828956     PISSN: 15622479     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (32)

1. A. K. Jain, M. N. Murty and P. J. Flynn, "Data clustering: A review," ACM Computing Surveys, vol.31, no.3, pp. 264-323, 1999.
2. A. D. Gordon, Classification, CRC Press, 1999.
3. H. H. Bock and E. Diday, Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, Springer-Verlag, 2000.
4. L. Billard and E. Diday, "From the statistics of data to the statistics of knowledge: Symbolic data analysis," Journal of the American Statistical Association, vol.98, no.462, pp. 470-487, 2003.
5. K. C. Gowda and E. Diday, "Symbolic clustering using a new dissimilarity measure," Pattern Recognition, vol.24, no.6, pp. 567-578, 1991.
6. K. C. Gowda and E. Diday, "Symbolic clustering using a new similarity measure," IEEE Trans. on Systems Man Cybernetic, vol.22, pp. 368-378, 1992.
7. M. Ichino and H. Yaguchi, "Generalized Minkowski metrics for mixed feature type data analysis," IEEE Trans. on Systems Man Cybernetic, vol.24, no.4, pp. 698-708, 1994.
8. K. C. Gowda and T. R. Ravi, "Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity," Pattern Recognition, vol.28, no.8, pp. 1277-1282, 1995.
9. K. C. Gowda and T. R. Ravi, "Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity," Pattern Recognition Letter, vol.16, pp. 647-652, 1995.
10. M. Chavent, "A monothetic clustering method," Pattern Recognition Letter, vol.19, pp. 989-996, 1998.
11. K. C. Gowda and T. R. Ravi, "Clustering of symbolic objects using gravitational approach," IEEE Trans. on Systems Man Cybernetic, vol.29, no.6, pp. 888-894, 1999.
12. D. S. Guru, B. B. Kiranagi, and P. Nagabhushan, "Multivalued type proximity measure and concept of mutual similarity value useful for clustering symbolic patterns," Pattern Recognition Letter, vol.25, pp. 1203-1213, 2004.
13. D. S. Guru and B. B. Kiranagi, "Multivalued type dissimilarity measure and concept of mutual dissimilarity value for clustering symbolic patterns," Pattern Recognition, vol.38, pp. 151-256, 2005.
14. E. Diday and P. Brito, Symbolic cluster analysis: Conceptual and Numerical Analysis of Data, Springer-Verlag, pp. 5-84, 1989.
15. H. Ralambondrainy, "A conceptual version of the k-means algorithm," Pattern Recognition Letter, vol.16, pp. 1147-1157, 1995.
16. A. D. Gordon, An iteractive relocation algorithm for classifying symbolic data. In: Gaul, W. et al. (Eds.), Data Analysis: Scientific Modeling and Practical Application, Springer-Verlag, pp. 7-23, 2000.
17. th Annual Conference of the Gesellschaft für Klassifikation e.V. University of Munich, March 13, 2001.
18. H. -H. Bock, "Clustering algorithms and Kohonen maps for symbolic data," Journal of the Japanese Society of Computational Statistics, vol.15, pp. 1-13, 2002.
19. M. Chavent and Y. Lechevallier, Dynamical clustering algorithm of interval data: Optimization of an adequacy criterion based on Hausdorff distance. In: Sokolowski, A., Bock, H.-H. (Eds.), Classification, Clustering and Data Analysis, Springer-Verlag, Heidelberg, pp. 53-59, 2002.
20. R. M. C. R. Souza, and F. A. T. De Carvalho, "Clustering of interval data based on city-block distances," Pattern Recognition Letter, vol.25, no.3, pp. 353- 365, 2004.
21. F. A. T. De Carvalho, R. M. C. R. Souza, M. Chavent, and Y. Lechevallier, "Adaptive Hausdorff distances and dynamic clustering of symbolic data," Pattern Recognition Letter, vol.27, no.3, pp. 167-179, 2006.
22. F. A. T. De Carvalho, "Fuzzy c-means clustering methods for symbolic interval data," Pattern Recognition Letter, vol.28, no.6, pp. 423-437, 2007.
23. Chen-Chia Chuang, Jin-Tsong Jeng, and C. W. Tao, "Interval competitive agglomeration clustering algorithm", Expert System with Applications, vol.37, no.9, pp. 6567-6578, 2010.
24. H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applocations in computer vision," IEEE Trans. Pattern Anal. Mach. Intell., vol.21, pp. 450-465, 1999.
25. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, New Jersey, 1988.
26. R. N. Dave and R. Krishnapurum, "Robust clustering methods: A unified view," IEEE Trans. Fuzzy System, vol.5, pp. 270-293, 1997.
27. Chen-Chia Chuang, Jin-Tsong Jeng, and C. W. Tao, "Hybrid robust approach for TSK fuzzy modeling with outliers", Expert Systems with Applications, vol.36, no.5, pp. 8925-8931, 2009.
28. Yih-Guang Lue, Tsu-Tian Lee, and Wei-Yen Wang, "Observer-based Adaptive Fuzzy-Neural Control for Unknown Nonlinear Dynamical Systems," IEEE Trans. System Man and Cybernetics-Part B, vol.29, no.5, pp. 583-591, October 1999.
29. Wei-Yen Wang, Tsu-Tian Lee, and Ching-Lang Liu and Chi-Hsu Wang, "Function Approximation Using Fuzzy Neural Networks with Robust Learning Algorithm," IEEE Trans. System Man and Cybernetics Part B, vol.27, no.4, pp. 740-747, August 1997.
30. Chen-Chia Chuang, "Annealing Robust Fuzzy Neural Networks for Modeling of Molecular Autoregulatory Feedback Loop Systems," International Journal of Fuzzy Systems, vol.10, no.1, pp. 11-17, 2008.
31. Chen-Chia Chuang, Jin-Tsong Jeng, and C. W. Tao, "Two-Stages Support Vector Regression for Fuzzy Neural Networks with Outliers," International Journal of Fuzzy Systems, vol.11, no.1, pp. 20-28, 2009.
32. Chen-Chia Chuang, Shun-Feng Su, Jin-Tsong Jeng, and Chih-Ching Hsiao, "Robust Support Vector Regression Networks for Function Approximation With Outliers," IEEE Trans. Neural Networks, vol. 13, no.6, pp. 1322-1330, 2002.