Analysis of measures of quantitative association rules

Authors:
M. Martínez-Ballesteros;J. C. Riquelme
Affiliations:
Department of Computer Science, University of Seville, Spain;Department of Computer Science, University of Seville, Spain
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Year:
2011

Citing 16
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Abstract

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.