Name
Abstract | ||
|---|---|---|
The practical success of association rule mining depends heavily on the criterion to choose among the many rules often mined. Many rule quality measures exist in the literature. We propose a protocol to evaluate the evaluation measures themselves. For each association rule, we measure the improvement in accuracy that a commonly used predictor can obtain from an additional feature, constructed according to the exceptions to the rule. We select a reference set of rules that are helpful in this sense. Then, our evaluation method takes into account both how many of these helpful rules are found near the top rules for a given quality measure, and how near the top they are. We focus on seven association rule quality measures. Our experiments indicate that multiplicative improvement and (to a lesser extent) support and leverage (a.k.a. weighted relative accuracy) tend to obtain better results than the other measures. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41398-8_7 | ADVANCES IN INTELLIGENT DATA ANALYSIS XII |
Keywords | Field | DocType |
Association rules,Feature Extraction,Prediction,Support,Confidence,Lift,Leverage,Improvement | Data mining,Leverage (finance),Multiplicative function,Pattern recognition,Computer science,Feature extraction,Association rule learning,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
8207 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 19 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José L. Balcázar | 1 | 701 | 62.06 |
| Francis Dogbey | 2 | 1 | 0.35 |