6533b861fe1ef96bd12c4c7c
RESEARCH PRODUCT
New Similarity Rules for Mining Data
Jerome H. FriedmanVito Di Gesùsubject
Similarity (network science)Association rule learningFeature vectorNoise (video)Data miningCluster analysiscomputer.software_genrecomputerMathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2006-01-01 |