Divisive Monothetic Clustering for Interval and Histogram-valued Data

Paula M. Brito (1) , Marie Chavent (2)

1 FEP - UP - Universidade do Porto - Faculdade de Economia & LIAAD-INESC TEC
2 CQFD - Quality control and dynamic reliability

In this paper we propose a divisive top-down clustering method designed for interval and histogram-valued data. The method provides a hierarchy on a set of objects together with a monothetic characterization of each formed cluster. At each step, a cluster is split so as to minimize intra-cluster dispersion, which is measured using a distance suitable for the considered variable types. The criterion is minimized across the bipartitions induced by a set of binary questions. Since interval-valued variables may be considered a special case of histogram-valued variables, the method applies to data described by either kind of variables, or by variables of both types. An example illustrates the proposed approach.

Domaines: Machine Learning [stat.ML]

Paula M. Brito, Marie Chavent. Divisive Monothetic Clustering for Interval and Histogram-valued Data. ICPRAM 2012 - 1st International Conference on Pattern Recognition Applications and Methods, Feb 2012, Portugal. pp.229-234. ⟨hal-00763215⟩