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Authors: Olivier Parisot ; Mohammad Ghoniem and Benoît Otjacques

Affiliation: Public Research Centre Gabriel Lippmann, Luxembourg

ISBN: 978-989-758-035-2

Keyword(s): Clustering Interpretation, Decision Trees, Data Preprocessing, Evolutionary Algorithms.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Analytics ; Data Engineering ; Data Management and Quality ; Data Management for Analytics ; Data Mining ; Data Modeling and Visualization ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Clustering is a popular technique for data mining, knowledge discovery and visual analytics. Unfortunately, cluster assignments can be difficult to interpret by a human analyst. This difficulty has often been overcome by using decision trees to explain cluster assignments. The success of this approach is however subject to the legibility of the obtained decision trees. In this work, we propose an evolutionary algorithm to cleverly preprocess the data before clustering in order to obtain clusters that are simpler to interpret with decision trees. A prototype has been implemented and tested to show the benefits of the approach.

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Paper citation in several formats:
Parisot, O.; Ghoniem, M. and Otjacques, B. (2014). Decision Trees and Data Preprocessing to Help Clustering Interpretation.In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 48-55. DOI: 10.5220/0005001300480055

@conference{data14,
author={Olivier Parisot. and Mohammad Ghoniem. and Benoît Otjacques.},
title={Decision Trees and Data Preprocessing to Help Clustering Interpretation},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={48-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005001300480055},
isbn={978-989-758-035-2},
}

TY - CONF

JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Decision Trees and Data Preprocessing to Help Clustering Interpretation
SN - 978-989-758-035-2
AU - Parisot, O.
AU - Ghoniem, M.
AU - Otjacques, B.
PY - 2014
SP - 48
EP - 55
DO - 10.5220/0005001300480055

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