Abstract
In recent years curve clustering problem has been handled in several applicative fields. However, most of the proposed approaches 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 an application on real data.
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Antonio, B., Romano, E., Verde, R. (2010). Outliers Detection Strategy for a Curve Clustering Algorithm. In: Palumbo, F., Lauro, C., Greenacre, M. (eds) Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03739-9_44
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DOI: https://doi.org/10.1007/978-3-642-03739-9_44
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