Possibilistic Clustering in Feature Space

Authors:
Maurizio Filippone;Francesco Masulli;Stefano Rovetta
Affiliations:
Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, and CNISM, Via Dodecaneso 35, I-16146 Genova, Italy;Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, and CNISM, Via Dodecaneso 35, I-16146 Genova, Italy;Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, and CNISM, Via Dodecaneso 35, I-16146 Genova, Italy
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

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Abstract

In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in feature space based on the ossibilistic approach to clustering. The proposed algorithms retain the properties of the possibilistic clustering, working as density estimators in feature space and showing high robustness to outliers, and in addition are able to model densities in the data space in a non-parametric way. One-Cluster Possibilistic C-Means in Feature Space can be seen also as a generalization of One-Class SVM.