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 |
|---|---|---|
2009 | 10.1016/j.eswa.2008.06.028 | Expert Syst. Appl. |
Keywords | Field | DocType |
interestingness,highest sensitivity score,data mining,bayesian network,mining bayesian network,mutual information,sensitivity measure,interesting pattern,ksl dataset,information theory,case study,association rules,new measure,bayesian networks,association rule | Information theory,Data mining,Variable-order Bayesian network,Computer science,Filter (signal processing),Association rule learning,Bayesian network,Artificial intelligence,Mutual information,Bayesian statistics,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
6 | 0.46 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rana Malhas | 1 | 10 | 1.88 |
| Zaher Al Aghbari | 2 | 180 | 25.73 |