Variability and noise in data-sets entries make hard the discover of important regularities among association rules in mining problems. The need exists for defining flexible and robust similarity measures between association rules. This paper introduces a new class of similarity functions, SF's, that can be used to discover properties in the feature space X and to perform their grouping with standard clustering techniques. Properties of the proposed SF's are investigated and experiments on simulated data-sets are also shown to evaluate the grouping performance. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Di Gesù, V., & Friedman, J. H. (2006). New similarity rules for mining data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3931 LNCS, pp. 179–187). Springer Verlag. https://doi.org/10.1007/11731177_26
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