Name
Abstract | ||
|---|---|---|
This paper introduces a new approach for mining if-then rules in databases with uncertainty and incompleteness. This approach is based on the combination of Generalization Distribution Table (GDT) and the rough set methodology. The GDT provides a probabilistic basis for evaluating the strength of a rule. It is used to find the rules with larger strengths from possible rules. Furthermore, the rough set methodology is used to find minimal relative reducts from the set of rules with larger strengths. The strength of a rule represents the uncertainty of the rule, which is influenced by both unseen instances and noises. By using our approach, a minimal set of rules with larger strengths can be acquired from databases with noisy, incomplete data. We have applied this approach to discover rules from some real databases. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/3-540-64383-4_30 | PAKDD |
Keywords | Field | DocType |
data mining,rough sets,generalization distribution,rough set | Information system,Data mining,Decision table,Information processing,Generalization,Computer science,Rough set,Artificial intelligence,Probabilistic logic,Machine learning,Knowledge acquisition | Conference |
Volume | ISSN | ISBN |
1394 | 0302-9743 | 3-540-64383-4 |
Citations | PageRank | References |
6 | 1.88 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Zhong | 1 | 2907 | 300.63 |
| Juzhen Dong | 2 | 214 | 17.05 |
| Setsuo Ohsuga | 3 | 960 | 222.02 |