data_symbolic

<p>Artificially generated symbolic interval data</p>

datasets

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) <doi:10.1007/BF02294245>,
Breiman, L. (1996), <doi:10.1007/BF00058655>,
Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>,
Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>,
Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>,
Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,
Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>,
Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

Andrzej Dudek

symbolicDA

Analysis of Symbolic Data

data_symbolic function

<p>3-dimensional array: 125 objects, 6 variables, third dimension represents  begining and end of interval, 5-class structure</p>

data_symbolic: Symbolic interval data

Description

Arguments

Format