Name
Abstract | ||
|---|---|---|
This paper describes two soft techniques, GDT-NN and GDTRS, for mining if-then rules in databases with uncertainty and incompleteness. The techniques are based on a Generalization Distribution Table (GDT), in which the probabilistic relationships between concepts and instances over discrete domains are represented. The GDT provides a probabilistic basis for evaluating the strength of a rule.We describe that a GDT can be represented by connectionist networks (GDT-NN for short), and if-then rules can be discovered by learning on the GDT-NN. Furthermore, we combine the GDT with the rough set methodology (GDT-RS for short). Thus, we can first find the rules with larger strengths from possible rules, and then 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. We compare GDT-NN with GDT-RS, and describe GDT-RS is a better way than GDT-NN for large, complex databases. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/3-540-69115-4_32 | Rough Sets and Current Trends in Computing |
Keywords | Field | DocType |
rough set methodology,possible rule,soft techniques,data mining,probabilistic relationship,mining if-then rule,connectionist network,complex databases,generalization distribution,if-then rule,probabilistic basis,larger strength,rough set | Information system,Data mining,Information processing,Decision table,Computer science,Decision support system,Rough set,Artificial intelligence,Probabilistic logic,Machine learning,Connectionism | Conference |
ISBN | Citations | PageRank |
3-540-64655-8 | 3 | 0.96 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Zhong | 1 | 2907 | 300.63 |
| Juzhen Dong | 2 | 214 | 17.05 |
| Setsuo Ohsuga | 3 | 960 | 222.02 |