Showing papers by "Jerome H. Friedman published in 2005"
08 Jun 2005
TL;DR: A new class of similarity functions, SF's, are introduced that can be used to discover properties in the feature space X and to perform their grouping with standard clustering techniques.
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.
2 citations