Name
Abstract | ||
|---|---|---|
In this paper, we present a new measure of interestingness to discover interesting patterns based on the user’s background knowledge, represented by a Bayesian network. The new measure (Sensitivity measure) captures the sensitivity of the Bayesian network to the patterns discovered by assessing the uncertainty-increasing potential of a pattern on the beliefs of the Bayesian network. Patterns that attain the highest sensitivity scores are deemed interesting. In our approach, mutual information (from information theory) came in handy as a measure of uncertainty. The Sensitivity of a pattern is computed by summing up the mutual information increases incurred by a pattern when entered as evidence/findings to the Bayesian network. We demonstrate the strength of our approach experimentally using the KSL dataset of Danish 70 year olds as a case study. The results were verified by consulting two doctors (internists). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/AICCSA.2008.4493535 | AICCSA |
Keywords | Field | DocType |
new measure,highest sensitivity score,bayesian network,interesting pattern,sensitivity measure,mutual information,ksl dataset,information theory,case study,data mining | Information theory,Data mining,Variable-order Bayesian network,Computer science,Bayesian network,Mutual information,Artificial intelligence,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-5322 | 978-1-4244-1968-5 | 1 |
PageRank | References | Authors |
0.37 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rana Malhas | 1 | 10 | 1.88 |
| Zaher Al Aghbari | 2 | 180 | 25.73 |