The general aim of cluster analysis is to build prototypes, or typologies of units that present similar characteristics. In this paper we propose an alternative approach based on consensus analysis of two different clustering methods to suitably obtain proto- types. The clustering methods used are fuzzy c-means (centre approach) and archetypal analysis (extreme approach). The consensus clustering is used to assess the correspon- dence between the clustering solutions obtained.
PROTOTYPE DEFINITION THROUGH CONSENSUS ANALYSIS BETWEEN FUZZY C-MEANS AND ARCHETYPAL ANALYSIS / Fordellone, Mario; Department of Statistical Sciences, Sapienza University of Rome; Palumbo, Francesco; Department of Political Sciences, Federico II University of Naples. - In: STATISTICA APPLICATA. - ISSN 1125-1964. - STAMPA. - 2:26(2015), pp. 141-162.
PROTOTYPE DEFINITION THROUGH CONSENSUS ANALYSIS BETWEEN FUZZY C-MEANS AND ARCHETYPAL ANALYSIS
FORDELLONE, MARIO;PALUMBO, Francesco;
2015
Abstract
The general aim of cluster analysis is to build prototypes, or typologies of units that present similar characteristics. In this paper we propose an alternative approach based on consensus analysis of two different clustering methods to suitably obtain proto- types. The clustering methods used are fuzzy c-means (centre approach) and archetypal analysis (extreme approach). The consensus clustering is used to assess the correspon- dence between the clustering solutions obtained.| File | Dimensione | Formato | |
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