SDA bibliography

Alexandre Cury, Christian Crémonab, Edwin Diday: Application of symbolic data analysis for structural modification assessment. Engineering Structures 32 (2010) 762-775.

C. Quantin, L. Billard, M. Touati, N. Andreu, Y. Cottin, M. Zeller, F. Afonso, G. Battaglia, D. Seck, G. Le Teuff, and E. Diday: Classification and Regression Trees on Aggregate Data Modeling: An Application in Acute Myocardial Infarction. Journal of Probability and Statistics, Vol 2011, ArtID 523937, 19 pages

M. Vrac, L. Billard, E. Diday, A. Chédin: Copula analysis of mixture models. Comput Stat.

Korenjak-Černe, S., Batagelj, V., Japelj Pavešić, B.: Clustering large data sets described with discrete distributions and its application on TIMSS data set. Statistical Analysis and Data Mining, Volume 4 (2011), Issue 2, pages 199–215.

A. Douzal-Chouakria, L. Billard and E. Diday: Principal Component Analysis for Interval-Valued Observations. Statistical Analysis and Data Mining, Vol. 4 (2011), 229-246

Kejžar, N., Korenjak-Černe, S., Batagelj, V.: Clustering of Distributions: A Case of Patent Citations, Journal of Classification (2011), Volume 28, Number 2, 156-183. local

E. Diday, “The symbolic approach in clustering and related methods of data analysis: the basic choices”, in ‘Classification and Related Methods of Data Analysis’, Proc. of IFCS'87, H.-H. Bock (ed.), Aachen, July 1987, North Holland, Amsterdam, pp. 673-684, 1988.

E. Diday, “Introduction à l'analyse des données symboliques”, Revue de Recherche Opérationnelle, 23 (2), pp. 193-236, 1989.

H-H. Bock and E. Diday (editors), Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data. Springer-Verlag, Berlin-Heidelberg, 2000.

E. Diday and M. Noirhomme-Fraiture (editors), Symbolic Data Analysis and the Sodas Software. Wiley, 2008.

L. Billard and E. Diday, Symbolic Data Analysis: Conceptual Statistics and Data Mining. Wiley, 2006.

L. Billard, “Brief overview of symbolic data and analytic issues”, Statistical Analysis and Data Mining, this issue, 2011.

E. Diday and M. Vrac, “Mixture decomposition of distributions by copulas in the symbolic data analysis framework”, Discrete Applied Mathematics, Volume 147, Issue 1, 1, pp. 27-41, April 2005.

E. Cuvelier, QAMML: Probability Distributions for Functional Data. PhD Thesis, University of Namur, Belgium, 2009.

M. Noirhomme-Fraiture, “Asymptotic behaviour in symbolic Markov chain”, in ‘Classification as a Tool for Research’, Proc. of the 11th IFCS Conf., Dresden, H. Locarek-Junge, C. Weihs (eds.), Springer, 2010.

G. Choquet, “Theory of capacities”, Annales de l'Institut Fourier, 5, pp. 131-295, 1954.

D. Dubois and H. Prade, “Properties of measures of information in evidence and possibility theories”, Fuzzy Sets and Systems 100 Supplement, pp. 35-49, 1999.

P. Walley, “Towards a unified theory of imprecise probability”, International Journal of Approximate Reasoning, Volume 24, Issues 2-3, pp. 125-148, May 2000.

R. Vignes, Caractérisation Automatique de Groupes Biologiques, PhD Thesis, University Paris VI, 1991.

F.A.T. De Carvalho, “Proximity coefficients between boolean symbolic objects”, in “New Approaches in Classification and Data Analysis”, E. Diday, E. et al. (eds.), Springer-Verlag, Berlin - Heidelberg, pp. 387-394, 1994.

M. Csernel and F.A.T. De Carvalho, (1999), “Usual operations with symbolic data under Normal Symbolic Form”, Applied Stochastic Models in Business and Industry, 15, pp. 241-257, 1999.

F.A.T. De Carvalho, P. Brito and H.-H. Bock, “Dynamic clustering for interval data based on L2 distance”, Computational Statistics, 21, 2, pp. 231-250, 2006.

A.P. Duarte Silva and P. Brito, “Linear discriminant analysis for interval data” Computational Statistics, 21, 2, pp. 289-308, 2006.

P. Bertrand and F. Goupil, “Descriptive statistics for symbolic data” in ‘Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data’, H.-H. Bock and E. Diday, (eds.), Springer, Heidelberg, pp. 106–124, 2000.

L. Billard and E. Diday, “From the statistics of data to the statistics of knowledge: Symbolic Data Analysis”, Journal of the American Statistical Association 98 (462), pp. 470–487, 2003.

L. Billard, “Dependencies and variation components of symbolic interval-valued data”, in ‘Selected Contributions in Data Analysis and Classification’, P. Brito et al (eds.), Springer, Heidelberg, pp. 3-12, 2007.

L. Billard, “Dependencies in bivariate interval-valued symbolic data”, in ‘Classification, clustering and data mining applications’, D. Banks, L. House, F.R. McMorris, P. Arabie, W. Gaul, (eds.), Proc. of the Meeting of the International Federation of Classification Societies (IFCS 2004), pp. 319-324, 2004.

L. Billard, “Sample covariance functions for complex quantitative data”, in Proc. of IASC2008, Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, Yokohama, Japan, December 2008.

L. Billard and E. Diday, “Descriptive statistics for interval-valued observations in the presence of rules”, Computational Statistics, 21, 2, pp. 187-210, 2006.

Chouakria, P. Cazes, and E. Diday, “Symbolic Principal Component Analysis” in ‘Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data’, H.-H. Bock and E. Diday, (eds.), Springer, Heidelberg, pp. 200–212, 2000.

P. Cazes, A. Chouakria, E. Diday and Y. Schektman, “Extensions de l’Analyse en Composantes Principales à des données de type intervalle”, Revue de Statistique Appliquée, 24, pp. 5-24, 1997.

Lauro and F. Palumbo, “Principal Component Analysis for non-precise data”, in ‘New Developments in Classification and Data Analysis’, M. Vichi, et al (eds.), Springer, pp. 173–184, 2005.

P. Giordani and H.A.L. Kiers, “A comparison of three methods for Principal Component Analysis of fuzzy interval data”, Computational Statistics & Data Analysis, special issue “The Fuzzy Approach to Statistical Analysis”, Volume 51, Number 1, pp. 379-397, November 2006.

O. Rodriguez, E. Diday and S. Winsberg, “Generalization of the Principal Components Analysis to histogram data” in Proc. 4th European Conference on Principles and Practice of Knowledge Discovery in Data Bases; Workshop on Symbolic Data Analysis, Lyon, 14, 2000.

O. Rodriguez and A. Pacheco, “Applications of histogram Principal Components Analysis”, in The 15th European Conference on Machine Learning (ECML) and the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Pisa, 2004.

M. Ichino, “Symbolic PCA for histogram-valued data”, in Proc. of IASC2008, Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, Yokohama, Japan, December 2008.

Lauro, R. Verde and A. Irpino, “Generalized canonical analysis”, in ‘Symbolic Data Analysis and the Sodas Software’, E. Diday and M. Noirhomme-Fraiture (eds.), Wiley, pp. 313-330, 2008.

P. Brito, “On the analysis of symbolic data”, in: ‘Selected Contributions in Data Analysis and Classification’, P. Brito et al (eds.), Springer, Heidelberg, pp. 13-22, 2007.

M. Chavent, “Normalized k-means clustering of hyper-rectangles”, in Proceedings of the XIth International Symposium of Applied Stochastic Models and Data Analysis (ASMDA 2005), Brest, France, pp. 670-677, 2005.

F. Esposito, D. Malerba and A. Appice, “Dissimilarity and matching”, in ‘Symbolic Data Analysis and the Sodas Software’, E. Diday and M. Noirhomme-Fraiture (eds.), Wiley, pp. 123-148, 2008.

Diday and F. Esposito, “An introduction to symbolic data analysis and the SODAS software”, Intelligent Data Analysis, 7, pp. 583-602, 2003.

R.M.C.R. de Souza and F.A.T. De Carvalho, “Clustering of interval data based on City-Block distances”, Pattern Recognition Letters, v. 25, n. 3, pp. 353-365, 2004.

R.M.C.R. de Souza, F.A.T. De Carvalho and C.P. Tenorio “Two partitional methods for interval-valued data using Mahalanobis distances”, IBERAMIA, pp. 454-463, 2004.

M. Chavent, F.A.T. De Carvalho, Y. Lechevallier and R. Verde, “New clustering methods for interval data”, Computational Statistics, Volume 21, Number 2, pp. 211-229, 2006.

F.A.T. De Carvalho and R.M.C.R.de Souza, “Unsupervised pattern recognition models for mixed feature-type symbolic data”, Pattern Recognition Letters 31(5), pp. 430-443, 2010.

F.A.T. De Carvalho, M. Csernel and Y. Lechevallier, “Clustering constrained symbolic data”, Pattern Recognition Letters 30(11) pp. 1037-1045, 2009.

F.A.T. De Carvalho, “Fuzzy c-means clustering methods for symbolic interval data”, Pattern Recognition Letters, v. 28, pp. 423-437, 2007.

R.M.C.R. de Souza, F.A.T. De Carvalho and F.C.D. Silva, “Clustering of interval-valued data using adaptive squared Euclidean distances”, in Proc. ICONIP, pp. 775-780, 2004.

R.M.C.R. de Souza and F.A.T. De Carvalho, “Dynamic clustering of interval data based on adaptive Chebyshev distances”, Electronics Letters, v. 40, n. 11, pp. 658-659, 2004.

F.A.T. De Carvalho, R.M.C.R. de Souza, M. Chavent and Y. Lechevallier, “Adaptive Hausdorff distances and dynamic clustering of symbolic interval data”, Pattern Recognition Letters, Volume 27, Issue 3, pp. 167-179, 2006.

F.A.T. De Carvalho and Y. Lechevallier, “Partitional clustering algorithms for symbolic interval data based on single adaptive distances”, Pattern Recognition 42 (7), pp. 1223-1236, 2009.

F.A.T. De Carvalho and C.P. Tenorio, “Fuzzy k-means clustering algorithms for interval-valued data based on adaptive quadratic distances”, Fuzzy Sets and Systems 161(23), pp. 2978-2999, 2010.

Hardy and N. Kasaro, “A new clustering method for interval data”, Mathématiques et sciences humaines, no 187, pp. 79-91, 2009.

Hardy and J. Baune, “Clustering and validation of interval data”, in ‘Selected Contributions in Data Analysis and Classification’, P. Brito et al (eds.), Springer, Heidelberg, pp. 69-82, 2007.

P. Brito, Analyse de Données Symboliques. Pyramides d'Héritage. PhD Thesis, University Paris-IX Dauphine, 1991.

P. Brito, “Use of pyramids in Symbolic Data Analysis”, in: ‘New Approaches in Classification and Data Analysis’, E. Diday et al. (eds.), Springer-Verlag, Berlin-Heidelberg, pp. 378-386, 1994.

P. Brito, “Symbolic objects: order structure and pyramidal clustering”, Annals of Operations Research, 55, pp. 277-297, 1995.

P. Brito, “Symbolic clustering of probabilistic data”, in ‘Advances in Data Science and Classification’, A. Rizzi, M. Vichi, M., H.-H. Bock (eds.), Springer-Verlag, Berlin-Heidelberg, pp. 385-390, 1998.

P. Brito and F.A.T. De Carvalho, “Symbolic clustering in the presence of hierarchical rules”, in Studies and Research Proceedings of the Conference on Knowledge Extraction and Symbolic Data Analysis (KESDA'98), Office for Official Publications of the European Communities, Luxembourg, pp. 119-128, 1999.

P. Brito and F.A.T. De Carvalho, “Symbolic clustering of constrained probabilistic data”, in ‘Exploratory Data Analysis in Empirical Research’, O. Opitz, M. Schwaiger (eds.), Springer Verlag, Heidelberg, pp. 12-21, 2002.

P. Brito and F.A.T. De Carvalho, “Hierarchical and pyramidal clustering”, in ‘Symbolic Data Analysis and the Sodas Software’, E. Diday and M. Noirhomme-Fraiture (eds.), Wiley, pp. 181-203, 2008.

M. Chavent, “A monothetic clustering method”, Pattern Recognition Letters, Volume 19, Issue 11, pp. 989-996, 1998.

H.-H. Bock, “Visualizing symbolic data by Kohonen maps”, in ‘Symbolic Data Analysis and the Sodas Software’, E. Diday and M. Noirhomme-Fraiture (eds.), Wiley, pp. 205-234, 2008.

Irpino and R. Verde, “A new Wasserstein based distance for the hierarchical clustering of histogram symbolic data” in ‘Data Science and Classification’, Proceedings of the Conference of the International Federation of Classification Societies (IFCS06), Springer, Berlin, pp. 185-192, 2006.

P. Brito and M. Ichino, “Symbolic clustering based on quantile representation”, presented at COMPSTAT’2110, Paris, 2010.

N.C. Lauro, R. Verde and F. Palumbo, “Factorial discriminant analysis on symbolic objects”, in ‘Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data’, H.-H. Bock and E. Diday, (eds.), Springer, Heidelberg, pp. 212–233, 2000.

N.C. Lauro, R. Verde and A. Irpino, “Factorial discriminant analysis”, in ‘Symbolic Data Analysis and the Sodas Software’, E. Diday and M. Noirhomme-Fraiture (eds.), Wiley, pp. 341-358, 2008.

J.P. Rasson and S. Lissoir, “Symbolic kernel discriminant analysis”, in ‘Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data’, H.-H. Bock and E. Diday, (eds.), Springer, Heidelberg, pp. 240-244, 2000.

J.P. Rasson, J.-Y. Pirçon, P. Lallemand and S. Adans, “Unsupervised divisive classification”, in ‘Symbolic Data Analysis and the Sodas Software’, E. Diday and M. Noirhomme-Fraiture (eds.), Wiley, pp. 149-156, 2008.

Périnel and Y. Lechevallier, “Symbolic discrimination rules”, in ‘Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data’, H.-H. Bock and E. Diday, (eds.), Springer, Heidelberg, pp. 244-265, 2000.

Ciampi, E. Diday, J. Lebbe, E. Périnel and R. Vignes, “Growing a tree classifier with imprecise data”, Pattern Recognition Letters, Vol. 21, no. 9, pp. 787-803, Aug. 2000.

M.C. Bravo Llatas and J.M. Santesmases, “segmentation trees for stratified data”, in ‘Analysis of Symbolic Data, Exploratory methods for extracting statistical information from complex data’, H.-H. Bock and E. Diday, (eds.), Springer, Heidelberg, pp. 266-293, 2000.

T.-N Do and F. Poulet, “Kernel methods and visualization for interval data mining”, in `Proc. of the Conf. on Applied Stochastic Models and Data Analysis, ASMDA 2005'. J. Janssen and P. Lenca (eds.), ENST Bretagne, 2005.

Carrizosa, J. Gordillo and F. Plastria, “Classification problems with imprecise data through separating hyperplanes” [Online]. Available at http://www.optimization-online.org/DB_FILE/2007/09/1781.pdf, 2007.

J. Síma, “Neural expert systems”, Neural Networks 8 (2), pp. 261-271, 1995.

S.J. Simoff, “ Handling uncertainty in neural networks: an interval approach”, in `Proc. of the IEEE International Conference on Neural Networks', IEEE, Washington D. C., pp. 606-610, 1996.

M. Beheshti, A. Berrached, A. de Korvin, C. Hu and O. Sirisaengtaksin, “On interval weighted freelayer neural networks”, in `Proc. of the 31st Annual Simulation Symposium' IEEE Computer Society Press, pp. 188-194, 1998.

Rossi and B. Conan Guez, “Multilayer perceptron on interval data”, in `Classification, Clustering and Data Analysis', K. Jajuga, A. Sokolowski and H.-H. Bock (eds.), Springer, Berlin, Heidelberg, New York, pp. 427-434, 2002.

L. Billard and E. Diday, “Regression analysis for interval-valued data, in ‘Data Analysis, Classification, and Related Methods’, Proceedings of the Seventh Conference of the International Federation of Classification Societies (IFCS00), Springer, pp. 369-374, 2000.

L. Billard and E. Diday, “Symbolic regression analysis”, in ‘Classification, Clustering and Data Analysis’, Proceedings of the Conference of the International Federation of Classification Societies (IFCS02), Springer, pp. 281-288, 2002.

E.A.L. Neto and F.A.T. De Carvalho, “Centre and range method for fitting a linear regression model to symbolic interval data”, Computational Statistics & Data Analysis, 52, 3, pp. 1500-1515, 2008.

E.A.L. Neto and F.A.T. De Carvalho, “Constrained linear regression models for symbolic interval-valued variables”, Computational Statistics & Data Analysis, 54, 2, pp. 333-347, 2010.

P. Teles and P. Brito, “Modelling interval time series data”, in Proceedings of the 3rd IASC World Conference on Computational Statistics and Data Analysis, Limassol, Cyprus, 2005.

A.L.S.Maia, F.A.T. De Carvalho and T.D. Ludermir, “Forecasting models for interval-valued time series”, Neurocomputing, 71 (16-18), pp. 3344-3352, 2008.

J. Arroyo, Métodos de Predicción para Series Temporales de Intervalos e Histogramas. PhD Thesis, Universidad Pontifícia Comillas, Madrid, Spain, 2008.

García-Ascanio and C. Maté, “Electric power demand forecasting using interval time series: A comparison between VAR and iMLP”, Energy Policy 38, pp. 715-725, 2009.

J. Arroyo, G. González-Rivera and C. Maté, “Forecasting with interval and histogram data. Some financial applications.”, in ‘Handbook of Empirical Economics and Finance’, A. Ullah, D. Giles, N. Balakrishnan, W. Schucany and E. Schilling, (eds.), Chapman and Hall/CRC, New York, 2010.

González-Rivera and J. Arroyo, “Autocorrelation function of the daily histogram time series of SP500 intradaily returns”, International Journal of Forecasting, 2010.

Han, Y., Hong, K. Lai and S. Wang, “Interval time series analysis with an application to the Sterling-Dollar exchange rate”, Journal of Systems Science and Complexity 21 (4), pp. 558-573. 2008.

Arroyo and C. Maté, “Forecasting histogram time series with k-nearest neighbours methods”, International Journal of Forecasting, 25, 182-207, 2009.

G. Birkoff, Lattice Theory, American Mathematical Society Colloquium Publications, Vol. XXV, 3rd edition, 1967.

M. Barbut and B. Monjardet, Ordre et Classification, Algèbre et Combinatoire, Tomes I et II, Hachette, Paris, 1970.

R. Wille, “Restructuring lattice theory: an approach based on hierarchies of concepts”, in Proceedings of the Symposium on Ordered Sets, I. Rival (ed.), Reidel, Dordrecht-Boston, pp. 445-470, 1982.

Ganter and R. Wille, Formal Concept Analysis – Mathematical Foundations, Berlin, Springer Verlag, 1999.

V. Duquenne and J.L. Guigues, “Familles minimales d’implication informatives résultant d’un tableau de données binaires”, Mathématiques et Sciences Humaines, 95, pp. 5-18, 1986.

G. Polaillon, Organisation et interprétation par les treillis de Galois de données de type multivalué, intervalle ou histogramme, PhD Thesis, Université Paris IX Dauphine, 1998.

G. Polaillon, “Interpretation and reduction of Galois lattices of complex data”, in Advances in ‘Data Science and Classification’, A. Rizzi, M. Vichi and H.-H. Bock (eds.), Springer-Verlag, pp. 433-440, 1998.

G. Polaillon and E. Diday, “Reduction of symbolic Galois lattices via hierarchies”, in Proceedings of the Conference on Knowledge Extraction and Symbolic Data Analysis (KESDA'98), Office for Official Publications of the European Communities, Luxembourg, pp. 137-143, 1999.

P. Brito and G. Polaillon, “Structuring probabilistic data by Galois lattices”, Mathématiques et Sciences Humaines - Mathematics and Social Sciences, (43ème année) nb.169, (1), pp. 77-104, 2005.

H-H. Bock, “Probabilistic modeling for symbolic data”, in COMPSTAT - Proceedings in Computational Statistics, P. Brito (ed.), Springer, Heidelberg, pp. 55-65, 2008.

P. Brito and A.P. Duarte Silva, “Modeling interval-data with Normal and Skew-Normal distributions” , in Proc. of IASC2008, Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis, Yokohama, Japan, December 2008.

Le-Rademacher and L. Billard, “Likelihood functions and some maximum likelihood estimators for symbolic data”, Journal of Statistical Planning and Inference, 141, pp.1593-1602, 2011.