Optimization of quality measures in association rule mining: an empirical study
- https://doi.org/10.2991/ijcis.2018.25905182How to use a DOI?
- Quality measures, Association rule mining, Optimization, Empirical study
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.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Cite this article
TY - JOUR AU - J. M. Luna AU - M. Ondra AU - H. M. Fardoun AU - S. Ventura PY - 2018 DA - 2018/11 TI - Optimization of quality measures in association rule mining: an empirical study JO - International Journal of Computational Intelligence Systems SP - 59 EP - 78 VL - 12 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2018.25905182 DO - https://doi.org/10.2991/ijcis.2018.25905182 ID - Luna2018 ER -