Creator:
Zhong, Ning ; Skowron, Andrzej
Contributor:
Grzymala-Busse, Jerzy - ed. ; Świniarski, Roman W. - ed. ; Zhong, Ning - ed. ; Ziarko, Wojciech - ed.
Title:
A rough set-based knowledge discovery process
Subtitle:
Rough Sets and Their Applications
Group publication title:
Subject and Keywords:
rough sets ; KDD process ; hybrid systems
Abstract:
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
Pages:
Source:
AMCS, volume 11, number 3 (2001) ; click here to follow the link