Abstract
In order to discover relevant information in a huge amount of data, a process commonly used in data mining consists in extracting logical association rules. As algorithms generally produce a large quantity of rules which hides the most interesting, it is essential to develop well-adapted rule mining tools which organize the rules and offer an intelligible representation of them.
In this paper, we focus on an approach based on the visualization of graphs modeling these association rule sets. The aesthetic criteria inherent to such representations are associated with combinatorial optimization problems unfortunately known to be NP hard. Moreover, in KDD applications it is necessary to introduce an additional criterion of stability when taking into account modifications in layout. We develop here a genetic algorithm for drawing association rule graphs which allows to satisfy readability constraints and to find very quickly new solutions close from the previous ones when slight modifications are inserted. Experimental results are presented, including the fitness function behavior with different GA parameters and graph sizes and a dynamic layout animation and an example on a corpus of real data is detailed.
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Guillet, F., Kuntz, P., Lehn, R. (1999). A Genetic Algorithm for Visualizing Networks of Association Rules. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_18
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DOI: https://doi.org/10.1007/978-3-540-48765-4_18
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