Data Visualization using Decision Trees and Clustering

Olivier Parisot, Yoanne Didry, Pierrick Bruneau, Benoît Otjacques


Decision trees are simple and powerful tools for knowledge extraction and visual analysis. However, when applied to complex datasets available nowadays, they tend to be large and uneasy to visualize. This difficulty can be overcome by clustering the dataset and representing the decision tree of each cluster independently. In order to apply the clustering more efficiently, we propose a method for adapting clustering results with a view to simplifying the decision tree obtained from each cluster. A prototype has been implemented, and the benefits of the proposed method are shown using the results of several experiments performed on the UCI benchmark datasets.


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Paper Citation

in Harvard Style

Parisot O., Didry Y., Bruneau P. and Otjacques B. (2014). Data Visualization using Decision Trees and Clustering . In Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014) ISBN 978-989-758-005-5, pages 80-87. DOI: 10.5220/0004740800800087

in Bibtex Style

author={Olivier Parisot and Yoanne Didry and Pierrick Bruneau and Benoît Otjacques},
title={Data Visualization using Decision Trees and Clustering},
booktitle={Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)},

in EndNote Style

JO - Proceedings of the 5th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2014)
TI - Data Visualization using Decision Trees and Clustering
SN - 978-989-758-005-5
AU - Parisot O.
AU - Didry Y.
AU - Bruneau P.
AU - Otjacques B.
PY - 2014
SP - 80
EP - 87
DO - 10.5220/0004740800800087