---
title: "Decision Trees in Numerical Attribute Spaces"
authors:
  - "Thierry Van de Merckt"
doi: ""
url: "https://ijcai.org/proceedings/1993/140"
pdf: "https://ijcai.org/proceedings/1993/0140.pdf"
venue: "Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence"
year: 1993
pages: "1016-1020"
track: "Machine Learning"
keywords:
---

# Decision Trees in Numerical Attribute Spaces

Thierry Van de Merckt

*Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence*, pages 1016-1020.

## Abstract

Research in Supervised Concept learning has not paid special attention to Numerical Attribute Spaces, although they present particular characteristics. One of them is that the combinatorial complexity of finding the correct disjunctions of the target concepts is much higher than with Nominal Attributes. In this paper, we argue that new kinds of heuristics using numerical similarities among training instances should be used. In the framework of TDIDT, we present a new approach that uses unsupervised heuristics issued from Cluster Analysis for selecting generalisation steps. Empirical tests show promising results, at least when the target concept is only partially represented in the training.