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 57 articles , 1 standard article )

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  1. Calcagnì, Antonio; Lombardi, Luigi; Avanzi, Lorenzo; Pascali, Eduardo: Multiple mediation analysis for interval-valued data (2020)
  2. Maharaj, Elizabeth Ann; Teles, Paulo; Brito, Paula: Clustering of interval time series (2019)
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  4. Sun, Yuying; Han, Ai; Hong, Yongmiao; Wang, Shouyang: Threshold autoregressive models for interval-valued time series data (2018)
  5. Hao, Peng; Guo, Junpeng: Constrained center and range joint model for interval-valued symbolic data regression (2017)
  6. Hron, Karel; Brito, Paula; Filzmoser, Peter: Exploratory data analysis for interval compositional data (2017)
  7. Le-Rademacher, J.; Billard, L.: Principal component analysis for histogram-valued data (2017)
  8. Wei, Yuan; Wang, Shanshan; Wang, Huiwen: Interval-valued data regression using partial linear model (2017)
  9. Blanco-Fernández, A.; González-Rodríguez, G.: Inferential studies for a flexible linear regression model for interval-valued variables (2016)
  10. Li, Wenhua; Guo, Junpeng; Chen, Ying; Wang, Minglu: A new representation of interval symbolic data and its application in dynamic clustering (2016)
  11. Chen, Meiling; Wang, Huiwen; Qin, Zhongfeng: Principal component analysis for probabilistic symbolic data: a more generic and accurate algorithm (2015)
  12. Duarte Silva, A. Pedro; Brito, Paula: Discriminant analysis of interval data: an assessment of parametric and distance-based approaches (2015)
  13. Giordani, Paolo: Lasso-constrained regression analysis for interval-valued data (2015)
  14. Guinot, Christiane; Malvy, Denis; Schémann, Jean-François; Afonso, Filipe; Haddad, Raja; Diday, Edwin: Strategies evaluation in environmental conditions by symbolic data analysis: application in medicine and epidemiology to trachoma (2015)
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  16. Teles, Paulo; Brito, Paula: Modeling interval time series with space-time processes (2015)
  17. D’Urso, Pierpaolo; De Giovanni, Livia; Massari, Riccardo: Self-organizing maps for imprecise data (2014)
  18. Kao, Chiun-How; Nakano, Junji; Shieh, Sheau-Hue; Tien, Yin-Jing; Wu, Han-Ming; Yang, Chuan-kai; Chen, Chun-houh: Exploratory data analysis of interval-valued symbolic data with matrix visualization (2014)
  19. Cabanes, Guénaël; Bennani, Younès; Destenay, Renaud; Hardy, André: A new topological clustering algorithm for interval data (2013) ioport
  20. de A. Carvalho, Francisco; Lechevallier, Yves; de Melo, Filipe M.: Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices (2013)

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