Simultaneous Clustering and Factorial Decomposition of Three-Way Datasets
CT3Clus Model
T3Clus Model
TWCFTA model
Simultaneous results attributes
Tandem results attributes
3FKMeans Model
TWFCTA model
Folding Matrix to Tensor by Mode.
Three-Mode Dataset Generator for Simulations
Random Membership Function Matrix Generator
One-run of the K-means clustering technique
PseudoF Score in the Full-Space
PseudoF Score in the Reduced-Space
Simultaneous Model
Simultaneous Model Constructor
Split Member of Largest cluster with An Empty cluster.
Tandem Class
Initializes an instance of the tandem model required by the tandem met...
Tensor Matricization
Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) <doi:10.1007/978-3-642-79999-0_1>.