A generalized approach to possibilistic clustering algorithms

International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems

Volumn 15, Issue SUPPL. 2, 2007, Pages 117-138

  Zhou, Jian ^a   Hung, Chih Cheng ^b  

^a TSINGHUA UNIVERSITY   (China)

^b SOUTHERN POLYTECHNIC STATE UNIVERSITY   (United States)

Author keywords

Fuzzy clustering; Fuzzy set theory; Possibilistic clustering

Indexed keywords

CLUSTERING ALGORITHMS; KNOWLEDGE BASED SYSTEMS; SET THEORY;

DATA GROUPING; FUZZY SET THEORY; POSSIBILISTIC CLUSTERING;

FUZZY CLUSTERING;

EID: 34249066103     PISSN: 02184885     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218488507004650     Document Type: Conference Paper

References (23)

1. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981).
2. R. Krishnapuram and J. M. Keller, A possibilistic approach to clustering, IEEE Transactions on Fuzzy Systems 1 (1993) 98-110.
3. R. Krishnapuram and J. M. Keller, The possibilistic c-means algorithm: insights and recommendations, IEEE Transactions on Fuzzy Systems 4 (1996) 385-393.
4. M.-S. Yang and K.-L. Wu, Unsupervised possibilistic clustering, Pattern Recognition 39 (2006) 5-21.
5. R. Krishnapuram, H. Frigui and O. Nasraoui, Fuzzy and possibilistic shell clustering algorihm and their application to boundary detection and surface approximation, IEEE Transactions on Fuzzy Systems 3 (1995) 29-60.
6. T. A. Runkler and J. C. Bezdek, Function approximation with polynomial membership functions and alternating cluster estimation, Fuzzy Sets and Systems 101 (1999) 207-218.
7. T. A. Runkler and J. C. Bezdek, Alternating cluster estimation: a new tool for clustering and function approximation, IEEE Transactions on Fuzzy Systems 7 (1999) 377-393.
8. L. A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338-353.
9. N. R. Pal and J. C. Bezdek, On cluster validity for fuzzy c-means model, IEEE Transactions on Fuzzy Systems 3 (1995) 370-379.
10. J. Yu, Q. Cheng and H. Huang, Analysis of the weighting exponent in the FCM, IEEE Iransactions on Systems, Man, and Cybernetics - Part B 34 (2004) 634-639.
11. H.-J. Zimmerman and P. Zysno, Quantifying vagueness in decision models, European Journal of Operational Research 22 (1985) 148-158.
12. J. Zhou and C. C. Hung, Credibilistic clustering approches, Technical Report, 2006.
13. B. Liu, Theory and Practice of Uncertain Programming (Physica-Verlag, Heidelberg, 2002).
14. B. Liu, Uncertainty Theory: An Introduction to its Axiomatic Foundations (Springer-Verlag, Berlin, 2004).
15. E. Anderson, The Irises of the gaspe peninsula, Bull. Am. IRIS Soc. 59 (1935) 2-5.
16. J. C. Bezdek, J. M. Keller, R. Krishnapuram, L. I. Kuncheva and N. R. Pal, Will the Iris data, please stand up?, IEEE Transactions on Fuzzy Systems 7 (1999) 368-369.
17. C. L. Blake and C. J. Merz, UCI repository of machine learning databases, a huge collection of artificial and real-world data sets, 1998. Available from: http://www.ics.uci.edu/~mlearn/MLRepository.html.
18. J. Cohen, A coefficient of agreement for nominal scales, Educational and Psychological Measurement 20 (1960) 37-46.
19. Z. Ma and R. L. Redmond, Tau coefficient for accuracy assessment of classification of remote sensing data, Photogrammetric Engineering and Remote Sensing 61 (1995) 435-439.
20. I. Gath and A. Geva, Unsupervised optimal fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (1989) 773-781.
21. L. A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems 1 (1978) 3-28.
22. L. A. Zadeh, The theory of approximate reasoning, Machine Intelligence 9 149-194 (J. Hayes, D. Michie and L. Mikulich, Eds., New York, Halstead Press, 1979).
23. B. Liu, A survey of entropy of fuzzy variables, Journal of Uncertain Systems 1 (2007) 1-10.