Fuzzy Kohonen clustering networks for interval data

Neurocomputing

Volumn 99, Issue , 2013, Pages 65-75

  de Almeida, Carlos W D ^a   de Souza, Renata M C R ^a   Candeias, Ana L B ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

Fuzzy Kohonen clustering network; Interval data; Symbolic data analysis; Weighted distances

Indexed keywords

DATA SETS; EUCLIDEAN DISTANCE; FUZZY KOHONEN CLUSTERING NETWORKS; FUZZY MEMBERSHIP VALUES; INTERVAL DATA; LEARNING RATES; SELF-ORGANIZING FUZZY NEURAL NETWORK; SYMBOLIC DATA ANALYSIS; WEIGHTED DISTANCE;

FUZZY NEURAL NETWORKS;

WEBSITES;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CLUSTER ANALYSIS; FUZZY SYSTEM; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 84867890353     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.06.019     Document Type: Article

References (48)

1. Jain A.K., Murty M.N., Flynn P.J. Data clustering: a review. ACM Comput. Surv. (CSUR) 1999, 31:264-323.
2. Abonyi J., Feil B. Cluster Analysis for Data Mining and System Identification 2007, Birkhauser Basel.
3. Bezdek J.C., Keller J., Krisnapuram R., Pal N.R. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets) 2005, Springer-Verlag New York, Inc., Secaucus, USA.
4. Bock H.-H., Diday E. Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data 2000, Springer.
5. Symbolic Data Analysis and the SODAS Software 2008, Wiley-Interscience. E. Diday, M. Noirhomme-Fraiture (Eds.).
6. Diday E., Esposito F. An introduction to symbolic data analysis and the sodas software. Intell. Data Anal. 2003, 7:583-601.
7. Billard L., Diday E. From the statistics of data to the statistics of knowledge: symbolic data analysis. J. Am. Stat. Assoc. 2003, 98:470-487.
8. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms 1981, Kluwer Academic Publishers, Norwell, MA, USA.
9. Gowda K.C., Ravi T.V. Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity. Pattern Recognition 1995, 28:1277-1282.
10. Gowda K., Ravi T.V. Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity. Pattern Recognition Lett. 1995, 16:647-652.
11. Chavent M. A monothetic clustering method. Pattern Recognition Lett. 1998, 19:989-996.
12. Ravi T.V., Gowda K.C. Clustering of symbolic objects using gravitational approach. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 1999, 29:888-894.
13. Guru D.S., Kiranagi B.B., Nagabhushan P. Multivalued type proximity measure and concept of mutual similarity value useful for clustering symbolic patterns. Pattern Recognition Lett. 2004, 25:1203-1213.
14. E. Diday, P. Brito, Symbolic Cluster Analysis, Springer-Verlag, pp. 45-84.
15. Ralambondrainy H. A conceptual version of the k-means algorithm. Pattern Recognition Lett. 1995, 16:1147-1157.
16. A.D. Gordon, An Interactive Relocation Algorithm for Classifying Symbolic Data, Springer-Verlag, Berlin, pp. 17-23.
17. V. Verde, F.A.T.D. Carvalho, Y. Lechevallier, A dynamical clustering algorithm for symbolic data, in: 25th Annual Conference of the German Classification Society, Tutorial on Symbolic Data Analysis, Munich, Germany, pp. 59-71.
18. Bock H.-H. Clustering algorithms and Kohonen maps for symbolic data. J. Jpn. Soc. Comput. Stat. 2003, 15:217-229.
19. Chavent M., Lechevallier Y. Dynamical Clustering Algorithm of Interval Data: Optimization of an Adequacy Criterion based on Hausdorff Distance 2002, Springer, Heidelberg, pp. 53-60.
20. de Souza R.M.C.R., de Carvalho F.A.T. Clustering of interval data based on city-block distances. Pattern Recognition Lett. 2004, 25:353-365.
21. de Carvalho F.A.T., de Souza R.M.C.R., Chavent M., Lechevallier Y. Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Lett. 2006, 27:167-179.
22. de Carvalho F.A.T., Lechevalliere Y. Partitional clustering algorithms for symbolic interval data based on single adaptive distances. Pattern Recognition 2009, 42:1223-1236.
23. de Carvalho F.A.T., Lechevallier Y. Dynamic clustering of interval-valued data based on adaptive quadratic distances. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 2009, 39:1295-1306.
24. El-Sonbaty Y., Ismail M.A. Fuzzy clustering for symbolic data. IEEE Trans. Fuzzy Syst. 1998, 6:195-204.
25. Yang M.-S., Hwang P.-Y., Chen D.-H. Fuzzy clustering algorithms for mixed feature variables. Fuzzy Sets Syst. 2004, 141:301-317.
26. de Carvalho F.A.T. Fuzzy c-means clustering methods for symbolic interval data. Pattern Recognition Lett. 2007, 28:423-437.
27. de Carvalho F.A.T., Tenório C.P. Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances. Fuzzy Sets Syst. 2010, 161:2978-2999.
28. Lee S., Lee E. Fuzzy neural networks. Math. Biosci. 1975, 23.
29. Haykin S. Neural Networks: A Comprehensive Foundation 1998, Prentice Hall PTR, Upper Saddle River, USA. 2nd ed.
30. J.C. Bezdek, E.C.-K. Tsao, N.R. Pal, Fuzzy Kohonen clustering networks, in: Proceedings of the First IEEE Conference on Fuzzy Systems, San Diego, USA, 1992.
31. Liu P., Li H. Fuzzy Neural Network Theory and Application 2004, World Scientific Press.
32. Keller J.M., Hunt D.J. Incorporating fuzzy membership function into the perceptron algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 1985, 7:693-699.
33. Huntsberger T.L., Ajjimarangsee P. Parallel self-organizing feature maps for unsupervised pattern recognition. Int. J. Gen. Syst. 1990, 16:357-372.
34. Tsao E.C.-K., Bezdek J.C., Pal N.R. Fuzzy Kohonen clustering networks. Pattern Recognition 1994, 27:757-764.
35. Kohonen T. Self-Organizing Maps 2001, Springer-Verlag. 3rd ed.
36. E. Diday, G. Govaert, Classification automatique avec distances adaptatives, RAIRO, Inf. 11 (1977) 329-349.
37. D.E. Gustafson, W.C. Kessel, Fuzzy clustering with a fuzzy covariance matrix, in: 17th Symposium on Adaptive Processes, vol. 17, pp. 761-766.
38. de Carvalho F.A.T., Tenorio C.P., Junior N.L.C. Partitional fuzzy clustering methods based on adaptive quadratic distances. Fuzzy Sets Syst. 2006, 157:2833-2857.
39. Hammer B., Villmann T. Generalized relevance learning vector quantization. Neural Networks 2002, 15:1059-1068.
40. Arnonkijpanich B., Hasenfuss A., Hammer B. Local matrix learning in clustering and applications for manifold visualization. Neural Networks 2010, 23:476-486.
41. Kästner M., Backhaus A., Geweniger T., Haase S., Seiffert U., Villmann T. Relevance learning in unsupervised vector quantization based on divergences. Advances in Self-Organizing Maps, Lecture Notes in Computer Science 2011, vol. 6731:90-100. Springer, Berlin, Heidelberg.
42. Jain A.K., Dubes R.C. Algorithms for Clustering Data 1988, Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
43. Kohonen T. Self-Organization and Associative Memory 1989, Springer-Verlag New York, Inc., New York, NY, USA. 3rd edition.
44. Ritter H., Martinetz T., Schulten K. Neural Computation and Self-Organizing Maps; An Introduction 1992, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
45. Hubert L., Arabie P. Comparing partitions. J. Classification 1985, 2:193-218.
46. Carvalho F.D., Brito P., Bock H.-H. Dynamic clustering for interval data based on l2 distance. Comput. Stat. 2006, 21:231-250.
47. Everitt B.S., Landau S., Leese M. Cluster Analysis 2009, Wiley. 4th ed.
48. Bobou A., Ribeyre F. Mercury in the food web: accumulation and transfer mechanisms. Met. Ions Biol. Syst. 1998, 289-319.