Name
Abstract | ||
|---|---|---|
In this paper, we define the concept of generalized rule for making classical deduction with imprecise data, stored both data and rules in a fuzzy relational database represented in the GEFRED model. We propose a way of measuring the imprecision related to the calculation of a fact based on the matching degree of the facts in the database and the facts calculated while expanding the rules. In order to achieve this, classical algorithms for deduction are not appropriated and we propose the modifications that have to be applied on a classical tuple-oriented algorithm in order to design a new algorithm for deducing from imprecise data with generalized rules. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1142/S0218488503002260 | International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems |
Keywords | Field | DocType |
relational database | Data mining,Database model,Relational database,Computer science,Fuzzy set operations,Fuzzy logic,Algorithm,Database design,Fuzzy number,Type-2 fuzzy sets and systems,Relational model | Journal |
Volume | Issue | ISSN |
11 | Supplement | 0218-4885 |
Citations | PageRank | References |
3 | 0.45 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignacio J. Blanco | 1 | 86 | 8.04 |
| Maria J. Martín-Bautista | 2 | 208 | 23.79 |
| Olga Pons | 3 | 274 | 21.67 |
| María Amparo Vila Miranda | 4 | 1182 | 93.57 |