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 48 articles , 1 standard article )

Showing results 1 to 20 of 48.
Sorted by year (citations)

1 2 3 next

  1. Duarte Silva, A. Pedro; Filzmoser, Peter; Brito, Paula: Outlier detection in interval data (2018)
  2. Sun, Yuying; Han, Ai; Hong, Yongmiao; Wang, Shouyang: Threshold autoregressive models for interval-valued time series data (2018)
  3. Hao, Peng; Guo, Junpeng: Constrained center and range joint model for interval-valued symbolic data regression (2017)
  4. Blanco-Fernández, A.; González-Rodríguez, G.: Inferential studies for a flexible linear regression model for interval-valued variables (2016)
  5. Li, Wenhua; Guo, Junpeng; Chen, Ying; Wang, Minglu: A new representation of interval symbolic data and its application in dynamic clustering (2016)
  6. Duarte Silva, A. Pedro; Brito, Paula: Discriminant analysis of interval data: an assessment of parametric and distance-based approaches (2015)
  7. Teles, Paulo; Brito, Paula: Modeling interval time series with space-time processes (2015)
  8. D’Urso, Pierpaolo; De Giovanni, Livia; Massari, Riccardo: Self-organizing maps for imprecise data (2014)
  9. 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)
  10. Cabanes, Guénaël; Bennani, Younès; Destenay, Renaud; Hardy, André: A new topological clustering algorithm for interval data (2013) ioport
  11. de A. Carvalho, Francisco; Lechevallier, Yves; de Melo, Filipe M.: Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices (2013)
  12. Le-Rademacher, J.; Billard, L.: Principal component histograms from interval-valued observations (2013)
  13. Guo, Junpeng; Li, Wenhua; Li, Chenhua; Gao, Sa: Standardization of interval symbolic data based on the empirical descriptive statistics (2012)
  14. Kim, Jaejik; Billard, L.: Dissimilarity measures and divisive clustering for symbolic multimodal-valued data (2012)
  15. Küchenhoff, Helmut; Augustin, Thomas; Kunz, Anne: Partially identified prevalence estimation under misclassification using the kappa coefficient (2012)
  16. Makosso-Kallyth, Sun; Diday, Edwin: Adaptation of interval PCA to symbolic histogram variables (2012)
  17. Yang, Miin-Shen; Hung, Wen-Liang; Chen, De-Hua: Self-organizing map for symbolic data (2012)
  18. Zuccolotto, Paola: Principal component analysis with interval imputed missing values (2012)
  19. de A. Lima Neto, Eufrásio; Cordeiro, Gauss M.; de A. Carvalho, Francisco: Bivariate symbolic regression models for interval-valued variables (2011)
  20. de Carvalho, Francisco de A. T.; Tenório, Camilo P.: Fuzzy (K)-means clustering algorithms for interval-valued data based on adaptive quadratic distances (2010)

1 2 3 next