Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items In A Preference Matrix

14 Pages Posted: 23 Oct 2018 Last revised: 31 Oct 2018

See all articles by Mariangela Sciandra

Mariangela Sciandra

University of Palermo - d/SEAS

Antonio D'Ambrosio

University of Naples Federico II - Faculty of Economics

Antonella Plaia

University of Palermo - d/SEAS

Date Written: July 6, 2018

Abstract

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 configurations and PCU solutions will be carried out through Procrustes analysis.

Keywords: Projetion pursuit, Preference data, Clustering rankings

Suggested Citation

Sciandra, Mariangela and D'Ambrosio, Antonio and Plaia, Antonella, Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items In A Preference Matrix (July 6, 2018). d/SEAS Working Paper No. 18-6, Available at SSRN: https://ssrn.com/abstract=3209215 or http://dx.doi.org/10.2139/ssrn.3209215

Mariangela Sciandra (Contact Author)

University of Palermo - d/SEAS

Viale delle Scienze, edificio 13
Palermo, 90124
Italy

Antonio D'Ambrosio

University of Naples Federico II - Faculty of Economics ( email )

Via Cintia, Monte S. Angelo
Napoli, 80126
Italy

Antonella Plaia

University of Palermo - d/SEAS

Viale delle Scienze, edificio 13
Palermo, 90124
Italy

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