Implements simultaneous T3Clus and 3FKMeans integrating an alpha value between 0 and 1 inclusive for a weighted result.
fit.ct3clus(
model,
X_i_jk,
full_tensor_shape,
reduced_tensor_shape,
alpha = 0.5
)# S4 method for simultaneous
fit.ct3clus(
model,
X_i_jk,
full_tensor_shape,
reduced_tensor_shape,
alpha = 0.5
)
Output attributes accessible via the '@' operator.
U_i_g0 - Initial object membership function matrix
B_j_q0 - Initial factor/component matrix for the variables
C_k_r0 - Initial factor/component matrix for the occasions
U_i_g - Final/updated object membership function matrix
B_j_q - Final/updated factor/component matrix for the variables
C_k_r - Final/updated factor/component matrix for the occasions
Y_g_qr - Derived centroids in the reduced space (data matrix)
X_i_jk_scaled - Standardized dataset matrix
BestTimeElapsed - Execution time for the best iterate
BestLoop - Loop that obtained the best iterate
BestIteration - Iteration yielding the best results
Converged - Flag to check if algorithm converged for the K-means
nConverges - Number of loops that converged for the K-means
TSS_full - Total deviance in the full-space
BSS_full - Between deviance in the reduced-space
RSS_full - Residual deviance in the reduced-space
PF_full - PseudoF in the full-space
TSS_reduced - Total deviance in the reduced-space
BSS_reduced - Between deviance in the reduced-space
RSS_reduced - Residual deviance in the reduced-space
PF_reduced - PseudoF in the reduced-space
PF - Weighted PseudoF score
Labels - Object cluster assignments
Fs - Objective function values for the KM best iterate
Enorm - Average l2 norm of the residual norm.
Initialized simultaneous model.
Matricized tensor along mode-1 (I objects).
Dimensions of the tensor in full space.
Dimensions of tensor in the reduced space.
0<alpha>1 hyper parameter. Model is T3Clus when alpha=1 and 3FKMeans when alpha=0.
tucker1966simuclustfactor t3clussimuclustfactor 3FKMeanssimuclustfactor VichiRocciKierssimuclustfactor
fit.t3clus fit.3fkmeans simultaneous
X_i_jk = generate_dataset()$X_i_jk
model = simultaneous()
ct3clus = fit.ct3clus(model, X_i_jk, c(8,5,4), c(3,3,2), alpha=0.5)
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