Published July 13, 2016
| Version v1
Publication
Selecting the best measures to discover quantitative association rules
Description
The majority of the existing techniques to mine association rules typically use the support and the confidence to
evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly assess
their quality due to some inherent drawbacks they present. A review of the literature reveals that there exist many
measures to evaluate the quality of the rules, but that the simultaneous optimization of all measures is complex and
might lead to poor results. In this work, a principal components analysis is applied to a set of measures that evaluate
quantitative association rules' quality. From this analysis, a reduced subset of measures has been selected to be
included in the fitness function in order to obtain better values for the whole set of quality measures, and not only
for those included in the fitness function. This is a general-purpose methodology and can, therefore, be applied to
the fitness function of any algorithm. To validate if better results are obtained when using the function fitness
composed of the subset of measures proposed here, the existing QARGA algorithm has been applied to a wide
variety of datasets. Finally, a comparative analysis of the results obtained by means of the application of QARGA
with the original fitness function is provided, showing a remarkable improvement when the new one is used.
Abstract
Ministerio de Ciencia y Tecnología TIN2011-28956-C02Additional details
Identifiers
- URL
- https://idus.us.es/handle/11441/43558
- URN
- urn:oai:idus.us.es:11441/43558