PCU: un nuovo algoritmo per il raggruppamento di individui o di oggetti a partire da una matrice di preferenze.
In the framework of preference rankings, the interest can lie in clustering individuals
or items in order to reduce the complexity of the preference space for an easier interpretation
of collected data. The last years have seen a remarkable owering of works about the use of
decision tree for clustering preference vectors. As a matter of fact, decision trees are useful and
intuitive, but they are very unstable: small perturbations bring big changes. This is the reason
why it could be necessary to use more stable procedures in order to clustering ranking data.
In this work, a Projection Clustering Unfolding (PCU) algorithm for preference data will be
proposed in order to extract useful information in a low-dimensional subspace by starting from
an high but mostly empty dimensional space. Comparison between unfolding congurations
and PCU solutions will be carried out through Procrustes analysis.
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