Dual-centers type-2 fuzzy clustering framework and its verification and validation indices

Applied Soft Computing Journal

Volumn 47, Issue , 2016, Pages 600-613

  Golsefid, Samira Malek Mohamadi ^a   Fazel Zarandi, Mohammad Hossein ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Cluster center uncertainty; Dual centers clustering; Interval type 2 fuzzy clustering; PCM; Validation index; Verification index

Indexed keywords

ALGORITHMS; CLUSTER ANALYSIS; FUZZY CLUSTERING; FUZZY SETS; PARAMETER ESTIMATION; PULSE CODE MODULATION;

CLUSTER CENTERS; COMPUTATIONAL EXPERIMENT; DUAL-CENTERS CLUSTERING; FUZZY CLUSTERING MODELING; INTERVAL TYPE-2 FUZZY; VALIDATION AND VERIFICATION; VALIDATION INDEX; VERIFICATION-AND-VALIDATION;

CLUSTERING ALGORITHMS;

EID: 84929877020     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.05.018     Document Type: Article

References (32)

1. [1] Klir, G.J., Wierman, M.J., Uncertainty-Based Information. 1998, Physica-Verlag, Heidelberg, Germany.
2. [2] Zadeh, L.A., Fuzzy Sets and Systems, System Theory. 1965, Polytechnic Press, 29–39.
3. [3] Ruspini, E., Numerical methods for fuzzy clustering. Inf. Sci. 2 (1970), 319–350.
4. [4] Mendel, J., John, B., Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst., 10, 2002, 2.
5. [5] Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8 (1975), 199–249.
6. [6] Rhee, F., Hwang, Ch., A type-2 fuzzy C-means clustering algorithm. IFSA World Congress and 20th NAFIPS International Conference, vol. 4, 2001, 1926–1929.
7. [7] Han, J., Kamber, M., Data Mining: Concepts and Techniques. 2006, Morgan Kaufmann Publishers.
8. [8] Jain, A.K., Dubes, R.C., Algorithms for Clustering Data. 1988, Prentice Hall, Englewood Cliffs.
9. [9] Dunn, J.C., A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3 (1974), 32–57.
10. [10] Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function. 1981, Plenum Press, New York.
11. [11] Krishnapuram, R., Keller, J.M., A possibilistic approach to clustering. Trans. Fuzzy Syst., 1(2), 1993.
12. [12] Hwang, Ch., Rhee, F., Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. IEEE Trans. Fuzzy Syst., 15(1), 2007, 107.
13. [13] Ceylan, R., Ozbay, Y., Karlik, B., A novel approach for classification of ECG arrhythmias: type-2 fuzzy clustering neural network. Expert Syst. Appl. 36 (2009), 6721–6726.
14. [14] Tao, C.W., Unsupervised fuzzy clustering with multi-center clusters. Fuzzy Sets Syst. 128 (2002), 305–322.
15. [15] Abiyeva, R.H., Kaynak, O., lshanableha, T., Mamedova, F., A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization. Appl. Soft Comput. 11 (2011), 1396–1406.
16. [16] Choi, B., Rhee, F., Interval type-2 fuzzy membership function generation methods for pattern recognition. Inf. Sci. 179 (2009), 2102–2122.
17. [17] Aliev, R.A., Pedrycz, W., Guirimov, B.G., Aliev, R.R., Ilhan, U., Babagil, M.A., Mammadli, S., Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization. Inf. Sci. 181 (2011), 1591–1608.
18. [18] Ozbay, Y., Ceylan, R., Karlik, B., Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Syst. Appl. 38 (2011), 1004–1010.
19. [19] Fazel Zarandi, M.H., Gamasaee, R., Turksen, I.B., A type-2 fuzzy c-regression clustering algorithm for Takagi–Sugeno system identification and its application in the steel industry. Inf. Sci. 187 (2012), 179–203.
20. [20] Tlig, L., Sayadi, M., Fnaiech, F., A new fuzzy segmentation approach based on S-FCM type2 using LBP-GCO features. Signal Process.: Image Commun. 27 (2012), 694–708.
21. [21] Awasthi, P., Blum, A., Sheffet, O., Center-based Clustering under Perturbation Stability. September 30, 2011, Elsevier Preprint submitted.
22. [22] Turksan, B., An Ontological and Epistemological Perspective of Fuzzy Set Theory. 2006, Elsevier B.V.
23. [23] Mendel, J., Advances in type-2 fuzzy sets and systems. Inf. Sci. 177:1 (2007), 84–110.
24. [24] Kelley, C.T., Iterative Methods for Optimization. 1999, Society for Industrial and Applied Mathematics Philadelphia.
25. [25] Coyle, G., Exelby, D., The validation of commercial system dynamics models. Syst. Dyn. Rev., 16, 2000, 1.
26. [26] Fazel Zarandi, M.H., Faraji, M.R., Karbasian, M., An exponential cluster validity index for fuzzy clustering with crisp and fuzzy data. Trans. E: Industrial Eng. 17:December (2) (2010), 95–110.
27. [27] Pedrycz, W., A dynamic data granulation through adjustable fuzzy clustering. Pattern Recognit. Lett. 29 (2008), 2059–2066.
28. [28] Sugeno, M., Yasukawa, T., A fuzzy logic based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1 (1993), 7–31.
29. [29] Box, G.E.P., Jenkins, G.M., Time Series Analysis, Forecasting and Control. 1970, Holden-Day, San Francisco (CA, USA).
30. [30] Cordon, O., Herrera, F., Sanchez, L., Solving electrical distribution problems using hybrid evolutionary data analysis techniques. Appl. Intell. 10 (1999), 5–24.
31. [31] Rezaee, B., Fazel Zarandi, M.H., Data-driven fuzzy modeling for Takagi Sugeno Kang fuzzy system. Inf. Sci. 180 (2010), 241–255.
32. [32] Bezdek, J.C., Fuzzy Mathematics in Parttern Classification. (Ph.D. thesis), 1973, Cornell University.