RT Journal Article
T1 Optimization of quality measures in association rule mining: an empirical study
A1 Luna, J. M.
A1 Ondra, M.
A1 Fardoun, H. M.
A1 Ventura, S.
K1 Quality measures
K1 Association rule mining
K1 Optimization
K1 Empirical study
K1 Algorithms
AB In the association rule mining field many different quality measures have been proposed over time with the aim of quantifying the interestingness of each discovered rule. In evolutionary computation, many of these metrics have been used as functions to be optimized, but the selection of a set of suitable quality measures for each specific problem is not a trivial task. The aim of this paper is to review the most widely used quality measures, analyze their properties from an empirical standpoint and, as a result, ease the process of selecting a subset of them for tackling the task of mining association rules through evolutionary computation. The experimental analysis includes twenty metrics, thirty datasets and a diverse set of algorithms to describe which quality measures are related (or unrelated) so they should (or should not) be used at time. A series of recomendations are therefore provided according to which quality measures are easily optimized, what set of measures should be used to optimize the whole set of metrics, or which measures are hardly optimized by any other.
PB Springer Nature
SN 1875-6891
YR 2018
FD 2018-08-06
LK http://hdl.handle.net/10668/19205
UL http://hdl.handle.net/10668/19205
LA en
NO Luna JM, Ondra M, Fardoun HM, Ventura S. Optimization of quality measures in association rule mining: an empirical study. The International Journal Of Computational Intelligence Systems/International Journal Of Computational Intelligence Systems [Internet]. 1 de enero de 2018;12(1):59. Disponible en: https://doi.org/10.2991/ijcis.2018.25905182
NO This research was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund, project tiN2017-83445-P
DS RISalud
RD Apr 6, 2025