Ontology type: schema:Chapter Open Access: True
2007-01-01
AUTHORSMaurizio Filippone , Francesco Masulli , Stefano Rovetta
ABSTRACTIn 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. More... »
PAGES219-226
Applications of Fuzzy Sets Theory
ISBN
978-3-540-73399-7
978-3-540-73400-0
http://scigraph.springernature.com/pub.10.1007/978-3-540-73400-0_27
DOIhttp://dx.doi.org/10.1007/978-3-540-73400-0_27
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