The Forward Search (FS) represents a useful tool for clustering data that include outlying observations, because it provides a robust clustering method in conjunction with graphical tools for outlier identification. In this paper, we show that recasting FS clustering in the framework of normal mixture models can introduce some improvements: the problem of choosing a metric for clustering is avoided; membership degree is assessed by posterior probability; a testing procedure for outlier detection can be devised.
D. G. Calò (2011). Some developments in Forward Search Clustering. HEIDELBERG : Springer-Verlag [10.1007/978-3-642-13312-1_13].
Some developments in Forward Search Clustering
CALO', DANIELA GIOVANNA
2011
Abstract
The Forward Search (FS) represents a useful tool for clustering data that include outlying observations, because it provides a robust clustering method in conjunction with graphical tools for outlier identification. In this paper, we show that recasting FS clustering in the framework of normal mixture models can introduce some improvements: the problem of choosing a metric for clustering is avoided; membership degree is assessed by posterior probability; a testing procedure for outlier detection can be devised.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
