Analysis of Symbolic Data

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) , Breiman, L. (1996), , Hubert, L., Arabie, P. (1985), , Ichino, M., & Yaguchi, H. (1994), , Rand, W.M. (1971) , Calinski, T., Harabasz, J. (1974) , Breckenridge, J.N. (2000) , Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) , Walesiak, M., Dudek, A. (2008) , Dudek, A. (2007), ).


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install.packages("symbolicDA")

0.6-2 by Andrzej Dudek, 16 days ago


http://keii.ue.wroc.pl/symbolicDA


Browse source code at https://github.com/cran/symbolicDA


Authors: Andrzej Dudek , Marcin Pelka <[email protected]> , Justyna Wilk<[email protected]> (to 2017-09-20) , Marek Walesiak <[email protected]> (from 2018-02-01)


Documentation:   PDF Manual  


GPL (>= 2) license


Imports rgl, shapes, e1071, ade4, cluster, RSDA

Depends on clusterSim, XML


Depended on by mdsOpt.


See at CRAN