Classification and Clustering of Preference Rankings
Kemeny-equivalent augmented dissimilarity matrix
K-Median Cluster Component Analysis
Utility function
Normalized Degree of Concordance (NDC) and Adjusted Concordance Index ...
Determine a tree from the main tree-based structure
Kemeny-equivalent augmented unfolding
Utility function
Utility function
Path of a terminal node
Plot Kemeny equivalent augmented unfolding solution
Plot tree-based structure or pruning sequence of ranktree
Predict the median rankings for new observations
S3 methods for cca
S3 methods for kunfolding
S3 methods for ranktree
Recursive partitioning method for the prediction of preference ranking...
Utility function
S3 methods for ranktree
S3 methods for kunfolding
S3 methods for ranktree
Path of a terminal node
Validation of the tree for preference rankings
Tau_x rank correlation coefficient for vectors
Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering, and Kemeny-equivalent augmented unfolding. It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). The package depends on the 'ConsRank' R package. Options for validate the tree-based method are both test-set procedure and V-fold cross validation. The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index). The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function 'sacofSym' from the package 'smacof'. Essential references: D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) <doi:10.1080/00273171.2021.1899892>; D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) <doi:10.1007/s00357-020-09367-0>; D'Ambrosio, A., and Heiser, W.J. (2019) <doi:10.1007/s41237-018-0069-5>; D'Ambrosio, A., and Heiser W.J. (2016) <doi:10.1007/s11336-016-9505-1>; Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) <doi:10.1109/TFUZZ.2011.2179303>; Marden, J.J. <ISBN:0412995212>.