DISCOVERING THE STABLE CLUSTERS BETWEEN INTERESTINGNESS MEASURES

Xuan-Hiep Huynh, Fabrice Guillet, Henri Briand

Abstract

In this paper, dealing with association rules post-processing, we propose to study the correlations between 36 interestingness measures (IM), in order to better understand their behavior on data and finally to help the data miner chooses the best IMs. We used two datasets with opposite characteristics in which we extract two rulesets about 100000 rules, and the two subsets of the 1000 best rules according to IMs. The study of the correlation between IMs with PAM and AHC shows unexpected stabilities between the four ruleset, and more precisely eight stable clusters of IMs are found and described.

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Paper Citation


in Harvard Style

Huynh X., Guillet F. and Briand H. (2006). DISCOVERING THE STABLE CLUSTERS BETWEEN INTERESTINGNESS MEASURES . In Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-42-9, pages 196-201. DOI: 10.5220/0002493701960201


in Bibtex Style

@conference{iceis06,
author={Xuan-Hiep Huynh and Fabrice Guillet and Henri Briand},
title={DISCOVERING THE STABLE CLUSTERS BETWEEN INTERESTINGNESS MEASURES},
booktitle={Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2006},
pages={196-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002493701960201},
isbn={978-972-8865-42-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - DISCOVERING THE STABLE CLUSTERS BETWEEN INTERESTINGNESS MEASURES
SN - 978-972-8865-42-9
AU - Huynh X.
AU - Guillet F.
AU - Briand H.
PY - 2006
SP - 196
EP - 201
DO - 10.5220/0002493701960201