Xuan-Hiep Huynh, Fabrice Guillet, Henri Briand


Selecting interestingness measures has been an important problem in knowledge discovery in database. A lot of measures have been proposed to extract the knowledge from large databases and many authors have introduced the interestingness properties for selecting a suitable measure for a given application. Some measures are adequate for some applications but the others are not, and it is difficult to capture what the best measures for a given data set are. In this paper, we present a new approach implemented in a tool to select the groups or clusters of objective interestingness measures that are highly correlated in an application. The final goal relies on helping the user to select the subset of measures that is the best adapted to discover the best rules according to his/her preferences.


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Paper Citation

in Harvard Style

Huynh X., Guillet F. and Briand H. (2005). CLUSTERING INTERESTINGNESS MEASURES WITH POSITIVE CORRELATION . In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-19-8, pages 248-253. DOI: 10.5220/0002508502480253

in Bibtex Style

author={Xuan-Hiep Huynh and Fabrice Guillet and Henri Briand},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 972-8865-19-8
AU - Huynh X.
AU - Guillet F.
AU - Briand H.
PY - 2005
SP - 248
EP - 253
DO - 10.5220/0002508502480253