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A rough set-based knowledge discovery process

100%

Zhong N., Skowron A.

International Journal of Applied Mathematics and Computer Science

2001

tom 11

nr 3

603-619

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.

Helena Rasiowa, 1917-1994

81%

Bartol W., Orłowska E., Skowron A.

Banach Center Publications

1999

tom 46

nr 1

9-21

Ograniczanie wyników

1 Banach Center Publications

1 International Journal of Applied Mathematics and Computer Science

2 Skowron A.

1 Bartol W.

1 Orłowska E.

1 Zhong N.

1 2001

1 1999

Wyniki wyszukiwania

A rough set-based knowledge discovery process

Helena Rasiowa, 1917-1994