International Journal of Computational Intelligence Systems

Volume 12, Issue 1, November 2018, Pages 59 - 78

Optimization of quality measures in association rule mining: an empirical study

Authors
J. M. Luna, M. Ondra, H. M. Fardoun, S. Ventura
Corresponding Author
J. M. Luna
Available online November 2018.
Keywords
Quality measures; Association rule mining; Optimization; Empirical study
Abstract
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.
Copyright
© The authors.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - JOUR
AU  - Luna, J. M.
AU  - Ondra, M.
AU  - Fardoun, H. M.
AU  - Ventura, S.
DA  - 2018/11/01
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://www.atlantis-press.com/article/25905182
ID  - Luna2018
ER  -