The clustering tree algorithm is both a clustering approach and a multi-objective supervised learning method.
In the cluster analysis framework, the aim is to group objects in clusters, where the objects in the same cluster are similar in a certain sense. The clustering tree algorithm enables to perform this kind of task. We obtain a decision tree as a clustering structure. Thus, the deployment of the classification rule in the information system is really easy.
But we can also consider the clustering tree as an extension of the classification/regression tree because we can distinguish two set of variables: the explained (active) variables which are used to determine the similarities between the objects; the predictive (illustrative) variables which allows to describe the groups.
In this slides, we show the main features of this approach.
Keywords: cluster analysis, clustering, clustering tree, groups characterization
Slides: Clustering tree
References :
M. Chavent (1998), « A monothetic clustering method », Pattern Recognition Letters, 19, 989—996.
H. Blockeel, L. De Raedt, J. Ramon (1998), « Top-Down Induction of Clustering Trees », ICML, 55—63.
