In recent years curve clustering problem has been handled in several applicative fields. However, most of them are sensitive to outliers. This paper aims to deal with this problem in order to make a partition, obtained by using a Dynamic Curve Clustering Algorithm with free knots spline estimation, more robust. The approach is based on a leave-some-out strategy, which defines a rule on the distances distribution of the curves from the barycenters in order to identify outliers regions. The method is validated by two applications on real data.

Outliers detection strategy for a Curve Clustering Algorithm

BALZANELLA, Antonio;ROMANO, Elvira;VERDE, Rosanna
2010

Abstract

In recent years curve clustering problem has been handled in several applicative fields. However, most of them are sensitive to outliers. This paper aims to deal with this problem in order to make a partition, obtained by using a Dynamic Curve Clustering Algorithm with free knots spline estimation, more robust. The approach is based on a leave-some-out strategy, which defines a rule on the distances distribution of the curves from the barycenters in order to identify outliers regions. The method is validated by two applications on real data.
Balzanella, Antonio; Romano, Elvira; Verde, Rosanna
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/174224
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