- Tytuł:
- A Rough Set-Based Knowledge Discovery Process
- Autorzy:
-
Zhong, N.
Skowron, A. - Powiązania:
- https://bibliotekanauki.pl/articles/908370.pdf
- Data publikacji:
- 2001
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
baza danych
baza wiedzy
system hybrydowy
rough sets
KDD process
hybrid systems - Opis:
- The knowledge discovery from real-life databases is a multi-phase process consisting of numerous steps, including attribute selection, discretization of real-valued attributes, and rule induction. In the paper, we discuss a rule discovery process that is based on rough set theory. The core of the process is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) for discovering classification rules from databases with uncertain and incomplete data. The system is based on a combination of Generalization Distribution Table (GDT) and the Rough Set methodologies. In the preprocessing, two modules, i.e. Rough Sets with Heuristics (RSH) and Rough Sets with Boolean Reasoning (RSBR), are used for attribute selection and discretization of real-valued attributes, respectively. We use a slope-collapse database as an example showing how rules can be discovered from a large, real-life database.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 603-619
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł