SODAS

The author gives a short introduction to the SODAS software. SODAS (Symbolic Official Data Analysis System) is a modular software in which each statistical method (symbolic objects data base, distance matrix for symbolic objects, divisible classification of symbolic data, symbolic kernel discriminant analysis, symbolic description of groups, factorial discriminant analysis, principal component analysis, histograms and elementary statistics, segmentation tree for stratified data, decision tree, etc.) is manipulated as an icon and icons are linked in a chaining. A symbolic data analysis with SODAS software looks graphically like a chain with links the statistical methods. The top icon represents the symbolic data file. A chaining gathers a set of symbolic statistical methods applied to a specified SODAS file. The chaining editor is used to create, modify, launch, suppress or rename any chaining. In all cases consistency control are made if a method needs results from a preceding method


References in zbMATH (referenced in 39 articles , 1 standard article )

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  1. Li, Wenhua; Guo, Junpeng; Chen, Ying; Wang, Minglu: A new representation of interval symbolic data and its application in dynamic clustering (2016)
  2. Duarte Silva, A.Pedro; Brito, Paula: Discriminant analysis of interval data: an assessment of parametric and distance-based approaches (2015)
  3. D’Urso, Pierpaolo; De Giovanni, Livia; Massari, Riccardo: Self-organizing maps for imprecise data (2014)
  4. Cabanes, Guénaël; Bennani, Younès; Destenay, Renaud; Hardy, André: A new topological clustering algorithm for interval data (2013)
  5. de A.T.de Carvalho, Francisco; Lechevallier, Yves; de Melo, Filipe M.: Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices (2013)
  6. Le-Rademacher, J.; Billard, L.: Principal component histograms from interval-valued observations (2013)
  7. Guo, Junpeng; Li, Wenhua; Li, Chenhua; Gao, Sa: Standardization of interval symbolic data based on the empirical descriptive statistics (2012)
  8. Kim, Jaejik; Billard, L.: Dissimilarity measures and divisive clustering for symbolic multimodal-valued data (2012)
  9. Küchenhoff, Helmut; Augustin, Thomas; Kunz, Anne: Partially identified prevalence estimation under misclassification using the kappa coefficient (2012)
  10. Makosso-Kallyth, Sun; Diday, Edwin: Adaptation of interval PCA to symbolic histogram variables (2012)
  11. Yang, Miin-Shen; Hung, Wen-Liang; Chen, De-Hua: Self-organizing map for symbolic data (2012)
  12. de Carvalho, Francisco de A.T.; Tenório, Camilo P.: Fuzzy $K$-means clustering algorithms for interval-valued data based on adaptive quadratic distances (2010)
  13. Boutsinas, B.; Papastergiou, T.: On clustering tree structured data with categorical nature (2008)
  14. Diday, Edwin: Spatial classification (2008)
  15. Diday, Edwin (ed.); Noirhomme-Fraiture, Monique (ed.): Symbolic data analysis and the SODAS software (2008)
  16. Elghazel, Haytham; Kheddouci, Hamamache; Deslandres, Véronique; Dussauchoy, Alain: A graph b-coloring framework for data clustering (2008)
  17. Giusti, Antonio; Grassini, Laura: Cluster analysis of census data using the symbolic data approach (2008)
  18. Bravo, M.; García-Santesmases, José: Relative and absolute contributions to aid strata interpretation (2007)
  19. Tzitzikas, Yannis: Evolution of faceted taxonomies and CTCA expressions (2007)
  20. Billard, Lynne; Diday, Edwin: Symbolic data analysis: Conceptual statistics and data mining (2006)

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