Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances

Neural Networks

Volumn 46, Issue , 2013, Pages 124-132

  Hajjar, Chantal ^a   Hamdan, Hani ^a  

^a SUPELEC   (France)

Author keywords

Clustering; Interval data; Mahalanobis distance; Self organizing maps; Unsupervised learning

Indexed keywords

BATCH TRAINING ALGORITHMS; CLUSTERING; HIGH DIMENSIONAL DATA; INTERVAL DATA; INTERVAL-VALUED DATA; LOW-DIMENSIONAL SPACES; MAHALANOBIS DISTANCES; TOPOLOGY PRESERVATION;

CLUSTERING ALGORITHMS; NEURAL NETWORKS; UNSUPERVISED LEARNING;

CONFORMAL MAPPING;

ALGORITHM; ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; DIMENSIONAL MEASUREMENT ACCURACY; MAHALANOBIS DISTANCE; MATHEMATICAL ANALYSIS; PARTITION COEFFICIENT; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; SELF ORGANIZING MAP; SIMULATION; TRAINING;

CLUSTERING; INTERVAL DATA; MAHALANOBIS DISTANCE; SELF-ORGANIZING MAPS; UNSUPERVISED LEARNING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER);

EID: 84878595739     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2013.04.009     Document Type: Article

References (19)

1. Bock H.H. Clustering methods and Kohonen maps for symbolic data. Journal of the Japanese Society of Computational Statistics 2003, 15(2):217-229.
2. Cazes P., Chouakria A., Diday E., Schektman Y. Extension de l'analyse en composantes principales à des données de type intervalle. Revue de Statistique Appliquée 1997, XIV(3):5-24.
3. Chavent M., Lechevallier Y. Dynamical clustering of interval data: optimization of an adequacy criterion based on Hausdorff distance. Classification, clustering and data analysis 2002, 53-60. Springer, Berlin, Germany, Also in the proceedings of IFCS2002, Poland. K. Jajuga, A. Sokolowski, H.H. Bock (Eds.).
4. Chouakria, A. (1998). Extension des méthodes d'analyse factorielle à des données de type intervalle. Université Paris 9 Dauphine.
5. L2 distance. Computational Statistics 2006, 21(2):231-250.
6. De Carvalho, F. A. T., & Pacifico, L. D. S. (2011). Une version batch de l'algorithme SOM pour des données de type intervalle. In Actes des XVIIIéme rencontres de la société francophone de classification (pp. 99-102). Orléans, France.
7. De Souza R.M.C.R., De Carvalho F.A.T. Clustering of interval data based on city-block distances. Pattern Recognition Letters 2004, 25(3):353-365.
8. de Souza R.M.C.R., de Carvalho F.A.T., Tenório C.P., Lechevallier Y. Dynamic cluster methods for interval data based on Mahalanobis distances. Proceedings of the 9th conference of the international federation of classification societies 2004, 351-360. Springer-Verlag, Chicago, USA.
9. Diday E. La méthode des nuées dynamiques. Revue de Statistique Appliquée 1971, 19(2):19-34.
10. El Golli A., Conan-Guez B., Rossi F. Self-organizing maps and symbolic data. Journal of Symbolic Data Analysis (JSDA) 2004, 2(1).
11. Hajjar, C., & Hamdan, H. (2011). Self-organizing map based on L2 distance for interval-valued data. In IEEE international symposium on applied computational intelligence and informatics. Timisoara, Romania (pp. 317-322).
12. Hamdan, H. (2005). Développement de méthodes de classification pour le contrÔle par émission acoustique d'appareils à pression. Université de technologie de Compiègne.
13. Hamdan, H., & Govaert, G. (2003). Classification de données de type intervalle via l'algorithme EM. In XXXVèmes journées de statistique, SFdS. Lyon, France (pp. 549-552).
14. Hamdan, H., & Govaert, G. (2004). Int-EM-CEM algorithm for imprecise data. Comparison with the CEM algorithm using Monte Carlo simulations. In IEEE international conference on cybernetics and intelligent systems. Singapore (pp. 410-415).
15. Hamdan, H., & Govaert, G. (2005). Mixture model clustering of uncertain data. In IEEE international conference on fuzzy systems. Reno, Nevada, USA (pp. 879-884).
16. Hubert L., Arabie P. Comparing partitions. Journal of Classification 1985, 2:193-218.
17. Kiviluoto, K. (1996). Topology preservation in self-organizing maps. In International conference on neural networks. Washington, D.C., USA (pp. 294-299).
18. Kohonen T. Self organization and associative memory 1984, Springer-Verlag. 2nd ed.
19. Rossi F., Conan-Guez B. Multi-layer perceptron on interval data. Classification, clustering, and data analysis 2002, 427-436. Springer, Berlin, Germany. K. Jajuga, A. Sokolowski, H.H. Bock (Eds.).