New clustering methods for interval data

Computational Statistics

Volumn 21, Issue 2, 2006, Pages 211-229

  Chavent, Marie ^a   De Carvalho, Francisco De A T ^b   Lechevallier, Yves ^c   Verde, Rosanna ^d  

^a UNIVERSITÉ BORDEAUX 1   (France)

^b FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

^c INRIA   (France)

^d SECOND UNIVERSITY OF NAPLES   (Italy)

Author keywords

Distances; Dynamic clustering; Interval data; Prototypes

Indexed keywords

EID: 33750148940     PISSN: 09434062     EISSN: 16139658     Source Type: Journal    
DOI: 10.1007/s00180-006-0260-0     Document Type: Article

References (20)

1. Bock, H.-H. (2001), Clustering algorithms and kohonen maps for symbolic data, in 'ICNCB Proceedings', Osaka, pp. 203 215.
2. Bock, H.-H. & Diday, E., eds (2000), Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data, Studies in classification, data analysis and knowledge organisation, Springer Verlag, Heidelberg.
3. Celeux, G., Diday, E., Govaert, G., Lechevallier, Y. & Ralambondrainy, H. (1989), Classification automatique des donnes, Dunod.
4. Chavent, M. (2004), An hausdorff distance between hyper-rectangles for clustering interval data, in D. Banks, L. House, F. McMorris, P. Arabie & W. Gaul, eds, 'Classification, Clustering, and Data Mining applications', Springer Verlag, pp. 333-339.
5. Chavent, M., De Carvalho, F. A. T., Lechevallier, Y. & Verde, R. (2003), 'Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle', Rev. Stat. Appliquées LI(4), 5-29.
6. Chavent, M. & Lechevallier, Y. (2002), Dynamical clustering of interval data, optimization of an adequacy criterion based on hausdorff distance, in K. Jajuga, A. Sokolowski & H.-H. Bock, eds, 'Classification, Clustering, and Data Analysis', Springer Verlag, Berlin, pp. 53-60.
7. De Carvalho, F. A. T. (1995), 'Histograms in symbolic data analysis.', Annals of Operations Research 55, 289-322.
8. De Carvalho, F. A. T. & Souza, R. M. C. (1998), Statistical proximity functions of boolean symbolic objects based on histograms, in A. Rizzi, M. Vichi & H.-H. Bock, eds, 'Advances in Data Science and Classification', Springer Verlag, pp. 391 396.
9. De Carvalho, F. A. T., Verde, R. & Lechevallier, Y. (1999), A dynamical clustering of symbolic objets based on a context dependent proximity measure., in H. e. a. Barcelar, ed., 'Proceedings of the IX International Symposium on Applied Stochastic Models and Data analysis', Universidade de Lisboa, pp. 237-242.
10. De Souza, R. M. C. R. & De Carvalho, F. A. T. (2004), 'Clustering of interval data based on city-block distances.', Pattern Recognition Letters 25(3), 353-365.
11. Diday, E. (1971), 'La méthode des nuées dynamiques', Rev. Stat. Appliquées 19(2).
12. Diday, E. (1988), The symbolic approach in clustering and related methods of data analysis: The basic choices, in H.-H. Bock, ed., 'Classification and related methods of data anlysis', North Holland, Amsterdam, pp. 673-684.
13. Diday, E. & Brito, P. (1989), Symbolic cluster analysis, in 0. Opitz & H.-H. Bock, eds, 'Conceptual and Numerical Analysis of Data', Springer Verlag, Berlin, pp. 45-84.
14. Diday, E. & Simon, J. C. (1976), Clustering analysis, in K. Pu, ed., 'Digital Pattern Classification', Springer Verlag, pp. 47 94.
15. Gordon, A. D. (2000), An iteractive relocation algorithm for classifying symbolic data., in W. e. a. Gaul, ed., 'Data Analysis: Scientific Modeling and Practical Application', Springer Verlag, Berlin, pp. 17-23.
16. Huttenlocher, D. P., Klanderman, G. A. & Rucklidge, W. J. (1993), 'Comparing images using the hausdorff distance', IEE Transaction on Pattern Analysis and Machine Intelligence 15, 850-863.
17. Nadler, S. B. J. (1978), Hyperspaces of sets, Marcel Dekker, Inc., New York.
18. Ralambondrainy, H. (1995), 'A conceptual version of the k-means algorithm.', Pattern Recognition Letters 16, 1147-1157.
19. Rote, G. (1991), 'Computing the minimum hausdorff distance between two point sets on a line under translation', Information Processing Letters 38, 123-127.
20. Verde, R., De Carvalho, F. A. T. & Lechevallier, Y. (2000), A dynamical clustering algorithm for multi-nominal data, in H. A. L. K. et al., ed., 'Data Analysis, Classification and Related methods', Springer Verlag, pp. 387-394.