Published July 14, 2016
| Version v1
Publication
Obtaining optimal quality measures for quantitative association rules
Description
There exist several works in the literature in which fitness functions based on a combination of weighted measures for
the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess
the quality of association rules could be obtained according to the values of the weights of the measures included in the
fitness function. Therefore, user's decision is very important in order to specify the weights of the measures involved in
the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the
measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called
QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights,
depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35
public datasets in order to show how the weights for confidence, support, amplitude and number of attributes
measures included in the fitness function have an influence on different quality measures according to several
minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in
measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered
depending on the group of measures to be optimized. Finally, the results obtained when using the recommended
weights for two real-world applications related to ozone and earthquakes are reported.
Abstract
Ministerio de Ciencia y Tecnología TIN2011-28956-C02Abstract
Junta de Andalucía P12- TIC-1728Abstract
Universidad Pablo de Olavide APPB813097Additional details
Identifiers
- URL
- https://idus.us.es/handle/11441/43608
- URN
- urn:oai:idus.us.es:11441/43608