Name
Abstract | ||
|---|---|---|
We consider a recent proposal to filter association rules on the basis of their novelty: the confidence boost. We develop appropriate mathematical tools to understand it in the presence of negated attributes, and explore the effect of applying it to association rules with negations. We show that, in many cases, the notion of confidence boost allows us to obtain reasonably sized output consisting of intuitively interesting association rules with negations. |
Year | Venue | Keywords |
|---|---|---|
2010 | KDIR 2010: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND INFORMATION RETRIEVAL | Association mining,Transactional dataset,Negated items,Confidence,Support,Confidence boost |
Field | DocType | Citations |
Data mining,Negation,Computer science,Filter (signal processing),Association rule learning,Artificial intelligence,Machine learning | Conference | 3 |
PageRank | References | Authors |
0.40 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José L. Balcázar | 1 | 701 | 62.06 |
| Cristina Tîrnauca | 2 | 12 | 3.26 |
| Marta E. Zorrilla | 3 | 51 | 16.05 |