RULE EXTRACTION FROM MEDICAL DATA WITHOUT DISCRETIZATION OF NUMERICAL ATTRIBUTES

Juan L. Domínguez-Olmedo, Jacinto Mata, Victoria Pachón, Manuel J. Maña

2012

Abstract

Association rule mining is a popular technique used to find associations between attributes in a dataset. When using deterministic algorithms, if the attributes have numerical values the usual approach is to discretize them defining proper intervals. But the discretization can notably affect the quality of the rules generated. This work presents a method based on a deterministic exploration of the interval search space without a previous discretization of the numerical attributes. It has been applied to medical data from an atherosclerosis study. The quality of the obtained rules seems to support this method as a valid alternative for this kind of rule extraction.

References

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Paper Citation


in Harvard Style

L. Domínguez-Olmedo J., Mata J., Pachón V. and J. Maña M. (2012). RULE EXTRACTION FROM MEDICAL DATA WITHOUT DISCRETIZATION OF NUMERICAL ATTRIBUTES . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012) ISBN 978-989-8425-88-1, pages 397-400. DOI: 10.5220/0003784603970400


in Bibtex Style

@conference{healthinf12,
author={Juan L. Domínguez-Olmedo and Jacinto Mata and Victoria Pachón and Manuel J. Maña},
title={RULE EXTRACTION FROM MEDICAL DATA WITHOUT DISCRETIZATION OF NUMERICAL ATTRIBUTES},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)},
year={2012},
pages={397-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003784603970400},
isbn={978-989-8425-88-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)
TI - RULE EXTRACTION FROM MEDICAL DATA WITHOUT DISCRETIZATION OF NUMERICAL ATTRIBUTES
SN - 978-989-8425-88-1
AU - L. Domínguez-Olmedo J.
AU - Mata J.
AU - Pachón V.
AU - J. Maña M.
PY - 2012
SP - 397
EP - 400
DO - 10.5220/0003784603970400