In many applications we are interested in finding clusters of data that share the same properties, like linear shape. We propose a hierarchical clustering procedure that merges groups if they are fitted well by the same linear model. The representative orthogonal model of each cluster is estimated robustly using iterated LQS regressions. We apply the method to two artificial datasets, providing a comparison of results against other non-hierarchical methods that can estimate linear clusters.

Hierarchical clustering by means of model grouping

AGOSTINELLI, Claudio;PELLIZZARI, Paolo
2006-01-01

Abstract

In many applications we are interested in finding clusters of data that share the same properties, like linear shape. We propose a hierarchical clustering procedure that merges groups if they are fitted well by the same linear model. The representative orthogonal model of each cluster is estimated robustly using iterated LQS regressions. We apply the method to two artificial datasets, providing a comparison of results against other non-hierarchical methods that can estimate linear clusters.
From Data and Information Analysis to Knowledge Engineering. Proceedings of the 29th annual conference of the German Classification Society, editors: Myra Spiliopoulou; Rudolf Kruse; Christian Borgelt; Andreas Nurnberger; Wolfgang Gaul
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/16051
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