Robust fuzzy clustering via trimming and constraints

Advances in Intelligent Systems and Computing

Volumn 456, Issue , 2017, Pages 197-204

  Dotto, Francesco ^a   Farcomeni, Alessio ^a   García Escudero, Luis Angel ^b   Mayo Iscar, Agustín ^b  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b UNIVERSITY OF VALLADOLID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

SHAPE MEMORY EFFECT; TRIMMING;

CLUSTERWISE LINEAR REGRESSIONS; DATA SET; FEASIBLE ALGORITHMS; MULTIVARIATE NORMAL; ROBUST FUZZY CLUSTERING; SPURIOUS CLUSTER; STATISTICAL PROBLEMS; TUNING PARAMETER;

FUZZY CLUSTERING;

EID: 84988450750     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-42972-4_25     Document Type: Conference Paper

References (32)

1. Banerjee A, Davé RN (2012) Robust clustering. Wires Data Min Knowl 2:29-59
2. Bezdek JC (1981) Pattern recognition with fuzzy objective function algoritms. Plenum Press, New York
3. Davé RN (1991) Characterization and detection of noise in clustering. Pattern Recogn Lett 12:657-664
4. Davé RN, Krishnapuram R (1997) Robust clustering methods: a unified view. IEEE Trans Fuzzy Syst 5:270-293
5. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39:1-38
6. Dotto F, Farcomeni A, García-Escudero LA, Mayo-Iscar A (2016) A fuzzy approach to robust regression clustering. Submitted manuscript
7. Farcomeni A, Greco L (2015) Robust methods for data reduction. Chapman and Hall/CRC, Boca Raton, Florida
8. Fritz H, García-Escudero LA, Mayo-Iscar A (2013) Robust constrained fuzzy clustering. Inf Sci 245:38-52
9. García-Escudero LA, Gordaliza A, Matrán C, Mayo-Iscar A (2008) A general trimming approach to robust cluster analysis. Ann Stat 36:1324-1345
10. García-Escudero LA, Gordaliza A, Matrán C, Mayo-Iscar A (2010) A review of robust clustering methods. Adv Data Anal Classif 4:89-109
11. García-Escudero LA, Gordaliza A, San Martín R, Mayo-Iscar A (2010) Robust clusterwise linear regresin through trimming. Comput Stat data Anal 54:3057-3069
12. Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11:773-781
13. Gustafson EE, Kessel WC (1979) Fuzzy clustering with a fuzzy covariance matrix. Proceedings of the IEEE lnternational conference on fuzzy systems, San Diego, pp 761-766 (1979)
14. Hathaway RJ,Bezdek JC (1993) Switching regression models and fuzzy clustering. IEEE Trans Fuzzy Syst 1:195-204
15. Hosmer DW (1974) Maximun likelihood estimates of the parameters of a mixture of two regression lines. Commun Stat Theory Methods 3:995-1006
16. Kuo-Lung W, Miin-Shen Y, June-Nan H (2009) Alternative fuzzy switching regression. In: Proceedings of the international multiconference of engineers and computer scientist
17. Kim J, Krishnapuram R, Davé R (1996) Application of the least trimmed squares technique to prototype-based clustering. Pattern Recogn Lett 17:633-641
18. Klawonn F (2004) Noise clustering with a fixed fraction of noise. In: Lotfi A, Garibaldi JM (eds) Applications and science in soft computing. Springer, Berlin-Heidelberg, pp 133-138
19. Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1:98-110
20. Krishnapuram R, Keller JM (1996) The possibilistic C-means algorithm: Insights and recommandations. IEEE Trans Fuzzy Syst 4:385-393
21. Lenstra AK, Lenstra JK, Rinnoy Kan AHG, Wansbeek TJ (1982) Two lines least squares. Ann Discrete Math 16:201-211
22. Łeski J (2003) Towards a robust fuzzy clustering. Fuzzy Set Syst 137:215-233
23. Miyamoto S, Mukaidono M (1997) Fuzzy c-means as a regularization and maximum entropy approach. In: Proceedings of the 7th international fuzzy systems association world congress (IFSA’97), pp 86-92
24. Ritter G (2015) Robust cluster analysis and variable selection. Monographs on statistics and applied probability. Chapman & Hall/CRC, Boca Raton, Florida
25. Rousseeuw PJ, Trauwaert E, Kaufman L (1995) Fuzzy clustering with high contrast. J Comput Appl Math 64:81-90
26. Rousseeuw PJ, Kaufman L, Trauwaert E (1996) Fuzzy clustering using scatter matrices. Comput Stat Data Anal 23:135-151
27. Rousseeuw PJ, Van Driessen K (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41:212-223
28. Ruspini E (1969) A new approach to clustering. Inf Control 15:22-32
29. Späth H (1982) A fast algorithm for clusterwise regression. Computing 29:175-181
30. Trauwaert E, Kaufman L, Rousseeuw PJ (1991) Fuzzy clustering algorithms based on the maximum likelihood principle. Fuzzy Sets Syst 42:213-227
31. Wu KL, Yang MS (2002) Alternative c-means clustering algorithms. Pattern Recogn 35:2267-2278
32. Yang MS (1993) On a class of fuzzy classification maximum likelihood procedures. Fuzzy Set Syst 57:365-337