Name
Abstract | ||
|---|---|---|
The development of effective interestingness measures that help in interpretation and evaluation of the discovered knowledge is an active research area in data mining and machine learning. In this paper, we consider a new Bayesian confirmation measure for "if..., then..." rules proposed in [4]. We analyze this measure, called Z, with respect to valuable property M of monotonic dependency on the number of objects in the dataset satisfying or not the premise or the conclusion of the rule. The obtained results unveil interesting relationship between Zmeasure and two other simple and commonly used measures of rule support and anti-support, which leads to efficiency gains while searching for the best rules. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69731-2_54 | ICAISC |
Keywords | Field | DocType |
efficiency gain,data mining,best rule,rule support,interesting relationship,new monotonic interestingness measure,machine learning,new bayesian confirmation measure,active research area,monotonic dependency,effective interestingness measure,business information systems,satisfiability,association rule,computing | Data mining,Management information systems,Monotonic function,Computer science,Premise,Association rule learning,Artificial intelligence,Machine learning,Bayesian probability | Conference |
Volume | ISSN | Citations |
5097 | 0302-9743 | 7 |
PageRank | References | Authors |
0.52 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvatore Greco | 1 | 3977 | 266.79 |
| Roman Slowinski | 2 | 5561 | 516.06 |
| Izabela Szczęch | 3 | 56 | 7.90 |