Nekaj dodatne literature na temo simbolne analize podatkov najdete spodaj:
Ahn, J., Peng, M., Park, C., Jeon, Y. (2012). A resampling approach for interval-valued data regression. Stat. Anal. Data Min. 5, 336–348.
Batagelj, V., Kejžar, N., Korenjak-Černe, S. (2015a). Clustering of Modal Valued Symbolic Data. ArXiv e-prints, 1507.06683, July 2015.
Batagelj, V., Korenjak Černe, S., Kejžar, N. (2015b). Generalized ANOVA for SDA. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Billard, L., Diday, E. (2000). Regression analysis for interval-valued data. In: Proc. of IFCS’00, Belgium, pp. 369-374,Springer.
Billard, L., Diday, E. (2002). Symbolic Regression Analysis. In: Proc. IFCS’02, Poland, pp. 281-288, Springer.
Billard, L., Diday, E. (2003). From the Statistics of Data to the Statistics of Knowledge: Symbolic Data Analysis. JASA. Journal of the American Statistical Association. June, Vol. 98, N° 462.
Billard, L., Diday, E. (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Wiley.
Bock, H.-H., Diday, E. (eds.) (2000), Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information From Complex Data, Berlin: Springer-Verlag.
Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J. (1984), Classification and Regression Trees, Belmont, CA: Wadsworth.
Brito, P. (1994). Use of Pyramids in Symbolic Data Analysis. In New Approaches in Classification and Data Analysis, eds. E. Diday, Y. Lechevallier,M. Schader, P. Bertrand, and B. Burtschy, Berlin: Springer-Verlag, pp. 378–386.
Brito, P. (1995). Symbolic Objects: Order Structure and Pyramidal Clustering. Annals of Operations Research, 55, 277–297.
Brito, P. (2000). Hierarchical and Pyramidal Clustering With Complete Symbolic Objects. In Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information From Complex Data, eds. H.-H. Bock and E. Diday, Berlin: Springer-Verlag, pp. 312–324
Brito, P., De Carvalho, F. A. T. (1999). Symbolic Clustering in the Presence of Hierarchical Rules. In Knowledge Extraction from Statistical Data, Luxembourg: European Commission Eurostat, pp. 119–128.
Cazes, P., Chouakria, A., Diday, E., Schektman, Y. (1997). Extensions de l’Analyse en Composantes Principales a des Donnees de Type Intervalle. Revue de Statistique Appliquee, 24, 5–24.
Chavent, M. (1998). A Monothetic Clustering Algorithm. Pattern Recognition Letters, 19, 989–996.
Chouakria, A. (1998). Extension des Methodes d’Analyse Factorielle a des Donees de Type Intervalle. Unpublished doctoral thesis, Université Paris Dauphine.
Chouakria, A., Diday, E., Cazes, P. (1999). An Improved Factorial Representation of Symbolic Objects. In Knowledge Extraction From Statistical Data, Luxembourg: European Commission Eurostat, pp. 301–305.
DeCarvalho, F. A. T., Verde, R., Lechevallier, Y. (1999). A Dynamical Clustering of Symbolic Objects Based on a Content Dependent Proximity Measure. Applied Statistical Models and Data Analysis, 15, 237–242.
Dias, S., Amaral, P., Brito, P. (2015). Linear Discriminant Analysis for Interval and Histogram Data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Dias, S., Brito, P. (2015). Regression for Symbolic Data. Powerpoint presentation. Symbolic Data Analysis: Taking Variability in Data into Account, ECI 2015 - Buenos Aires.
Diday, E. (1986). Orders and Overlapping Clusters by Pyramids. In Multidimensional Data Analysis, eds. J. De Leeuw, W. J. Heisen, J. J. Meulman, and F. Critchley, Leiden, Netherlands: DSWO Press, pp. 201–234.
Diday, E. (2015). Explanatory Power of a Symbolic Data Table. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Diday, E., Emilion, R. (2015). Symbolic Bayesian Networks. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Drago, C., Reale, A. (2015). Symbolic Data Analysis of Large Scale Spatial Network Data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Egozcue, J. J., Pawlowsky-Glahn, V. (2015). Compositional Analysis of Contingency Tables. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Giordani, P. (2014). Linear regression analysis for interval-valued data based on the Lasso technique. Adv. Data Anal. Classif. (2014) ISSN: 1862-5347.
Irpino, A., Verde, R. (2015). Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance. Adv Data Anal Classif 9(1), pp. 81-106.
Irpino, A., Verde, R., De Carvalho, F.A.T. (2015). Fuzzy clustering of distribution-valued data using an adaptive L2 Wasserstein distance. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Korenjak-Černe, S., Kejžar, N., Batagelj, V. (2015). A weighted clustering of population pyramids for the world's countries, 1996, 2001, 2006. Population studies, 69(1):105-120, 2015.
Košmelj, K., Billard, L. (2011). Clustering of population pyramids using Mallows’ L2 distance. Metodološki zvezki, 8, 1-15.
Lima Neto, E.d.A. (2015). Advances in regression models for interval variables: a copula based model. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Lima Neto, E.d.A., De Carvalho, F.A.T. (2008). Center and Range method for fitting a linear regression model to symbolic interval data. Computational Statistics & Data Analysis 52 (3), 1500-1515.
Lima Neto, E.d.A., De Carvalho, F.A.T. (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics & Data Analysis 54 (2), 333-347.
Lima Neto, E.d.A., Cordeiro, G.M., De Carvalho, F.A.T. (2011). Bivariate symbolic regression models for interval-valued variables. Journal of Statistical Computation and Simulation 81 (11), 1727-1744.
Maia, A., Carvalho, F. D. (2008). Fitting a least absolute deviation regression model on symbolic interval data. In Lecture Notes in Artificial Intelligence: Proceedings of the Ninth Brazilian Symposium on Artificial Intelligence, pp. 207–216. Springer-Verlag, Berlin.
Meco, A., Arroyo, J. (2015). Locally weighted learning methods for histogram data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Michalski, R. S., Diday, E., Stepp, R. E. (1981). A Recent Advance in Data Analysis: Clustering Objects Into Classes Characterized by Conjunctive Concepts. In Progress in Pattern Recognition, eds. L. Kanal and A. Rosenfeld, Amsterdam: North-Holland, pp. 33–56.
Michalski, R. S., Stepp, R. E. (1984). Learning from Observation: Conceptual Clustering. In Machine Learning, eds. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Berlin: Springer-Verlag, pp. 331–363.
Pawlowsky-Glahn, V., Egozcue, J. J. (2015). Sample space approach to compositional data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Polaillon, G. (2000). Pyramidal Classification for Internal Data Using Galois Lattice Reduction. In Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information From Complex Data, eds. H.-H. Bock and E. Diday, Berlin: Springer-Verlag, pp. 324–340.
Quinlan, J. R. (1986). Introduction of Decision Trees. Machine Learning, 1, 81–106.
Su, S. F., Chuang, C. C., Tao, C. W., Jeng, J. T., Hsiao, C. C. (2012). Radial basis function networks with linear interval regression weights for symbolic interval data. IEEE Trans. Syst., Man, Cybern., Part B Cybern., vol. 42, no. 1, pp. 69-80, Feb. 2012.
Xu, W. (2010). Symbolic Data Analysis: Interval-Valued Data Regression. PhD thesis, University of Georgia.
Yang, C., Chuang, C., Jeng, J., Tao, C. (2011). Constructing the linear regression models for the symbolic interval-values data using PSO algorithm. In Proc. International Conference on System Science and Engineering (ICSSE), Macau, China. 2011.
Ahn, J., Peng, M., Park, C., Jeon, Y. (2012). A resampling approach for interval-valued data regression. Stat. Anal. Data Min. 5, 336–348.
Batagelj, V., Kejžar, N., Korenjak-Černe, S. (2015a). Clustering of Modal Valued Symbolic Data. ArXiv e-prints, 1507.06683, July 2015.
Batagelj, V., Korenjak Černe, S., Kejžar, N. (2015b). Generalized ANOVA for SDA. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Billard, L., Diday, E. (2000). Regression analysis for interval-valued data. In: Proc. of IFCS’00, Belgium, pp. 369-374,Springer.
Billard, L., Diday, E. (2002). Symbolic Regression Analysis. In: Proc. IFCS’02, Poland, pp. 281-288, Springer.
Billard, L., Diday, E. (2003). From the Statistics of Data to the Statistics of Knowledge: Symbolic Data Analysis. JASA. Journal of the American Statistical Association. June, Vol. 98, N° 462.
Billard, L., Diday, E. (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Wiley.
Bock, H.-H., Diday, E. (eds.) (2000), Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information From Complex Data, Berlin: Springer-Verlag.
Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J. (1984), Classification and Regression Trees, Belmont, CA: Wadsworth.
Brito, P. (1994). Use of Pyramids in Symbolic Data Analysis. In New Approaches in Classification and Data Analysis, eds. E. Diday, Y. Lechevallier,M. Schader, P. Bertrand, and B. Burtschy, Berlin: Springer-Verlag, pp. 378–386.
Brito, P. (1995). Symbolic Objects: Order Structure and Pyramidal Clustering. Annals of Operations Research, 55, 277–297.
Brito, P. (2000). Hierarchical and Pyramidal Clustering With Complete Symbolic Objects. In Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information From Complex Data, eds. H.-H. Bock and E. Diday, Berlin: Springer-Verlag, pp. 312–324
Brito, P., De Carvalho, F. A. T. (1999). Symbolic Clustering in the Presence of Hierarchical Rules. In Knowledge Extraction from Statistical Data, Luxembourg: European Commission Eurostat, pp. 119–128.
Cazes, P., Chouakria, A., Diday, E., Schektman, Y. (1997). Extensions de l’Analyse en Composantes Principales a des Donnees de Type Intervalle. Revue de Statistique Appliquee, 24, 5–24.
Chavent, M. (1998). A Monothetic Clustering Algorithm. Pattern Recognition Letters, 19, 989–996.
Chouakria, A. (1998). Extension des Methodes d’Analyse Factorielle a des Donees de Type Intervalle. Unpublished doctoral thesis, Université Paris Dauphine.
Chouakria, A., Diday, E., Cazes, P. (1999). An Improved Factorial Representation of Symbolic Objects. In Knowledge Extraction From Statistical Data, Luxembourg: European Commission Eurostat, pp. 301–305.
DeCarvalho, F. A. T., Verde, R., Lechevallier, Y. (1999). A Dynamical Clustering of Symbolic Objects Based on a Content Dependent Proximity Measure. Applied Statistical Models and Data Analysis, 15, 237–242.
Dias, S., Amaral, P., Brito, P. (2015). Linear Discriminant Analysis for Interval and Histogram Data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Dias, S., Brito, P. (2015). Regression for Symbolic Data. Powerpoint presentation. Symbolic Data Analysis: Taking Variability in Data into Account, ECI 2015 - Buenos Aires.
Diday, E. (1986). Orders and Overlapping Clusters by Pyramids. In Multidimensional Data Analysis, eds. J. De Leeuw, W. J. Heisen, J. J. Meulman, and F. Critchley, Leiden, Netherlands: DSWO Press, pp. 201–234.
Diday, E. (2015). Explanatory Power of a Symbolic Data Table. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Diday, E., Emilion, R. (2015). Symbolic Bayesian Networks. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Drago, C., Reale, A. (2015). Symbolic Data Analysis of Large Scale Spatial Network Data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Egozcue, J. J., Pawlowsky-Glahn, V. (2015). Compositional Analysis of Contingency Tables. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Giordani, P. (2014). Linear regression analysis for interval-valued data based on the Lasso technique. Adv. Data Anal. Classif. (2014) ISSN: 1862-5347.
Irpino, A., Verde, R. (2015). Linear regression for numeric symbolic variables: an ordinary least squares approach based on Wasserstein Distance. Adv Data Anal Classif 9(1), pp. 81-106.
Irpino, A., Verde, R., De Carvalho, F.A.T. (2015). Fuzzy clustering of distribution-valued data using an adaptive L2 Wasserstein distance. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Korenjak-Černe, S., Kejžar, N., Batagelj, V. (2015). A weighted clustering of population pyramids for the world's countries, 1996, 2001, 2006. Population studies, 69(1):105-120, 2015.
Košmelj, K., Billard, L. (2011). Clustering of population pyramids using Mallows’ L2 distance. Metodološki zvezki, 8, 1-15.
Lima Neto, E.d.A. (2015). Advances in regression models for interval variables: a copula based model. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Lima Neto, E.d.A., De Carvalho, F.A.T. (2008). Center and Range method for fitting a linear regression model to symbolic interval data. Computational Statistics & Data Analysis 52 (3), 1500-1515.
Lima Neto, E.d.A., De Carvalho, F.A.T. (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics & Data Analysis 54 (2), 333-347.
Lima Neto, E.d.A., Cordeiro, G.M., De Carvalho, F.A.T. (2011). Bivariate symbolic regression models for interval-valued variables. Journal of Statistical Computation and Simulation 81 (11), 1727-1744.
Maia, A., Carvalho, F. D. (2008). Fitting a least absolute deviation regression model on symbolic interval data. In Lecture Notes in Artificial Intelligence: Proceedings of the Ninth Brazilian Symposium on Artificial Intelligence, pp. 207–216. Springer-Verlag, Berlin.
Meco, A., Arroyo, J. (2015). Locally weighted learning methods for histogram data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Michalski, R. S., Diday, E., Stepp, R. E. (1981). A Recent Advance in Data Analysis: Clustering Objects Into Classes Characterized by Conjunctive Concepts. In Progress in Pattern Recognition, eds. L. Kanal and A. Rosenfeld, Amsterdam: North-Holland, pp. 33–56.
Michalski, R. S., Stepp, R. E. (1984). Learning from Observation: Conceptual Clustering. In Machine Learning, eds. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Berlin: Springer-Verlag, pp. 331–363.
Pawlowsky-Glahn, V., Egozcue, J. J. (2015). Sample space approach to compositional data. Powerpoint presentation, 5th SDA Workshop, November 17-19, 2015, Orléans, France.
Polaillon, G. (2000). Pyramidal Classification for Internal Data Using Galois Lattice Reduction. In Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information From Complex Data, eds. H.-H. Bock and E. Diday, Berlin: Springer-Verlag, pp. 324–340.
Quinlan, J. R. (1986). Introduction of Decision Trees. Machine Learning, 1, 81–106.
Su, S. F., Chuang, C. C., Tao, C. W., Jeng, J. T., Hsiao, C. C. (2012). Radial basis function networks with linear interval regression weights for symbolic interval data. IEEE Trans. Syst., Man, Cybern., Part B Cybern., vol. 42, no. 1, pp. 69-80, Feb. 2012.
Xu, W. (2010). Symbolic Data Analysis: Interval-Valued Data Regression. PhD thesis, University of Georgia.
Yang, C., Chuang, C., Jeng, J., Tao, C. (2011). Constructing the linear regression models for the symbolic interval-values data using PSO algorithm. In Proc. International Conference on System Science and Engineering (ICSSE), Macau, China. 2011.