fold

X_i_jk =&gt; X_i_j_k, X_j_ki =&gt; X_i_j_k, X_k_ij =&gt; X_i_j_k

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>.

Prosper Ablordeppey

simuclustfactor

Simultaneous Clustering and Factorial Decomposition of Three-Way
Datasets

Adelaide Freitas

Giorgia Zaccaria

fold function

<dl><dt>X</dt>
<dd>Data matrix to fold.</dd>
<dt>mode</dt>
<dd>Mode of operation.</dd>
<dt>shape</dt>
<dd>Dimension of original tensor.</dd></dl>

Arguments

Folding Matrix to Tensor by Mode. — fold

<dl>

<dt>X</dt>
<dd>Data matrix to fold.</dd>


<dt>mode</dt>
<dd>Mode of operation.</dd>


<dt>shape</dt>
<dd>Dimension of original tensor.</dd>

</dl>

fold: Folding Matrix to Tensor by Mode.

Description

Usage

Value

Arguments

Examples