Clustering Methods in Symbolic Data Analysis

Symbolic Data Analysis and the SODAS Software

Volumn , Issue , 2008, Pages 181-203

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

^a Centro de Informatica   (Brazil)

^b INRIA   (France)

^c Dipartimento di Studi Europei e Mediterranei   (Italy)

Author keywords

Allocation function and dissimilarity function; Categorical multi valued or interval descriptors; Classical dynamic clustering method; Contextdependent proximity function; Description potential function; Interval valued data clusters; Nearest neighbour algorithm criterion; Representation step and allocation step; Symbolic clustering algorithm; Total sum of squares (TSS), within sum of squares (WSS) and between sum of squares (BSS)

Indexed keywords

EID: 84948766830     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470723562.ch11     Document Type: Chapter

References (19)

1. Bock, H.-H. and Diday, E. (eds), (2000) Analysis of Symbolic Data. Springer Verlag. Berlin.
2. Celeux, G., Diday, E., Govaert, G., Lechevallier, Y. and Ralambondrainy, H. (1988) Classification Automatique des Données. Bordas, Paris.
3. Chavent, M. (1997) Analyse des données symboliques. Une méthode divisive de classification. Thesis, Université Paris IX Dauphine.
4. Chavent, M. and Lechevallier, Y. (2002) Dynamical clustering algorithm of interval data: optimization of an adequacy criterion based on Hausdorff distance. In A. Sokolowski and H.-H. Bock (eds), Classification, Clustering and Data Analysis, pp. 53-59. Springer-Verlag, Berlin.
5. Chavent, M., de Carvalho, F.A.T., Lechevallier, Y. and Verde, R. (2003) Trois nouvelles méthodes de classification automatique des données symboliques de type intervalle. Revue de Statistique Appliquée, 51(4), 5-29.
6. Chavent, M., de Carvalho, F.A.T., Lechevallier, Y. and Verde, R. (2006) New clustering methods for interval data. Computational Statistics, 21, 211-230.
7. de Carvalho, F.A.T. (1992) Méthodes descriptives en analyse des données symboliques. Doctoral thesis, Université Paris IX Dauphine.
8. de Carvalho, F.A.T. (1994) Proximity coefficients between Boolean symbolic objects. In E. Diday, Y. Lechevallier, M. Schader, P. Bertrand and B. Bustschy (eds), New Approaches in Classification and Data Analysis, pp. 387-394. Springer-Verlag, Berlin.
9. de Carvalho, F.A.T. and Souza, R.M.C. (1998) Statistical proximity functions of Boolean symbolic objects based on histograms. In A. Rizzi, M. Vichi and H.-H. Bock (eds), Advances in Data Science and Classification, pp. 391-396. Springer-Verlag, Berlin.
10. de Carvalho, F.A.T. and Souza, R.M.C. (1999) New metrics for constrained Boolean symbolic objects. In Studies and Research: Proceedings of the Conference on Knowledge Extraction and Symbolic Data Analysis (KESDA'98). pp. 175-187. Office for Official Publications of the European Communities, Luxembourg.
11. de Carvalho, F.A.T, Verde, R. and Lechevallier, Y. (1999) A dynamical clustering of symbolic objects based on a context dependent proximity measure. In H. Bacelar-Nicolau, F.C. Nicolau and J. Janssen (eds), Proc. IX International Symposium-ASMDA'99, pp. 237-242. LEAD, University of Lisbon.
12. Diday, E. (1971) La méthode des nuées dynamiques Revue de Statistique Appliquée, 19(2), 19-34.
13. Gordon, A.D. (1999) Classification. Chapman and Hall/CRC, Boca Raton, FL.
14. Gower, J.C. (1966) Some distance properties of latent root and vector methods using multivariate analysis. Biometrika, 53, 325-338.
15. Ichino, M. and Yaguchi, H. (1994). Generalized Minkowski metrics for mixed feature type data analysis. IEEE Transactions on Systems, Man and Cybernetics, 24, 698-708.
16. Lechevallier, Y. (1974) Optimisation de quelques critères en classification automatique et application à l'étude des modifications des protéines sériques en pathologie clinique. Thesis, Université Paris VI.
17. MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281-297. University of California Press, Berkeley.
18. Michalski, R.S., Stepp, R.E. and Diday, E. (1981) A recent advance in data analysis: Clustering objects into classes characterized by conjunctive concepts. In L.N. Kanal and A. Rosenfeld A. (eds), Progress in Pattern Recognition, pp. 33-56. North-Holland, Amsterdam.
19. Verde, R., de Carvalho, F.A.T. and Lechevallier, Y. (2000) A dynamical clustering algorithm for multi-nominal data. In H.A.L. Kiers, J.-P. Rasson, P.J.F. Groenen and M. Schader (eds), Data Analysis, Classification, and Related Methods, pp. 387-394. Springer-Verlag, Berlin.