New similarity rules for mining data

Authors:
Vito Di Gesù;Jerome H. Friedman
Affiliations:
Università di Palermo, DMA, Italy;Department of Statistics, Stanford University, Stanford, CA
Venue:
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Year:
2005

Citing 4
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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.