clustering with decision trees: divisive and agglomerative approach.
Lauriane Castin, Benoît Frénay · Repository of the University of Namur · 2018
Decision trees are mainly used to perform classification tasks. Samples are submitted to a test in each node of the tree and guided through the tree based on the result. Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples labels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters. In this paper, new split criteria and agglomeration algorithms are developed for clustering, with results comparable to other existing clustering techniques.