IN YOUR INTEREST - Objective Interestingness Measures for a Generative Classifier
Dominik Fisch, Edgar Kalkowski, Bernhard Sick, Seppo J. Ovaska
2011
Abstract
In a wide-spread definition, data mining is termed to be the “non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data”. In real applications, however, usually only the validity of data mining results is assessed numerically. An important reason is that the other properties are highly subjective, i.e., they depend on the specific knowledge and requirements of the user. In this article we define some objective interestingness measures for a specific kind of classifier, a probabilistic classifier based on a mixture model. These measures assess the informativeness, uniqueness, importance, discrimination, comprehensibility, and representativity of rules contained in this classifier to support a user in evaluating data mining results. With some simulation experiments we demonstrate how these measures can be applied.
DownloadPaper Citation
in Harvard Style
Fisch D., Kalkowski E., Sick B. and J. Ovaska S. (2011). IN YOUR INTEREST - Objective Interestingness Measures for a Generative Classifier . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 414-423. DOI: 10.5220/0003186404140423
in Bibtex Style
@conference{icaart11,
author={Dominik Fisch and Edgar Kalkowski and Bernhard Sick and Seppo J. Ovaska},
title={IN YOUR INTEREST - Objective Interestingness Measures for a Generative Classifier},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={414-423},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003186404140423},
isbn={978-989-8425-40-9},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - IN YOUR INTEREST - Objective Interestingness Measures for a Generative Classifier
SN - 978-989-8425-40-9
AU - Fisch D.
AU - Kalkowski E.
AU - Sick B.
AU - J. Ovaska S.
PY - 2011
SP - 414
EP - 423
DO - 10.5220/0003186404140423