Abstract
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.
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Di Gesù, V., Friedman, J.H. (2006). New Similarity Rules for Mining Data. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_26
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DOI: https://doi.org/10.1007/11731177_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
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