This paper presents the analysis of relationships among different
interestingness measures of quality of association rules as first step
to select the best objectives in order to develop a multi-objective algorithm.
For this purpose, the discovering of association rules is based on
evolutionary techniques. Specifically, a genetic algorithm has been used
in order to mine quantitative association rules and determine the intervals
on the attributes without discretizing the data before. The algorithm
has been applied in real-word climatological datasets based on Ozone and
Earthquake data.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261
