onekmeans

Initializes centroids based on a given membership function matrix or randomly.
Iterate once over the input data to update the membership function matrix
assigning objects to the closest centroids.

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

onekmeans function

<dl><dt>Y_i_qr</dt>
<dd>Input data to group/cluster.</dd>
<dt>G</dt>
<dd>Number of clusters to find.</dd>
<dt>U_i_g</dt>
<dd>Initial membership matrix for the I objects.</dd>
<dt>seed</dt>
<dd>Seed for random values generation.</dd></dl>

Arguments

One-run of the K-means clustering technique — onekmeans

<dl>

<dt>Y_i_qr</dt>
<dd>Input data to group/cluster.</dd>


<dt>G</dt>
<dd>Number of clusters to find.</dd>


<dt>U_i_g</dt>
<dd>Initial membership matrix for the I objects.</dd>


<dt>seed</dt>
<dd>Seed for random values generation.</dd>

</dl>

onekmeans: One-run of the K-means clustering technique

Description

Usage

Value

Arguments

References

Examples