A rough set-based knowledge discovery process

Ning Zhong; Andrzej Skowron

International Journal of Applied Mathematics and Computer Science (2001)

  • Volume: 11, Issue: 3, page 603-619
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Zhong, Ning, and Skowron, Andrzej. "A rough set-based knowledge discovery process." International Journal of Applied Mathematics and Computer Science 11.3 (2001): 603-619. <http://eudml.org/doc/207522>.

@article{Zhong2001,
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.},
author = {Zhong, Ning, Skowron, Andrzej},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {KDD process; rough sets; hybrid systems; knowledge discovery; real-life databases},
language = {eng},
number = {3},
pages = {603-619},
title = {A rough set-based knowledge discovery process},
url = {http://eudml.org/doc/207522},
volume = {11},
year = {2001},
}

TY - JOUR
AU - Zhong, Ning
AU - Skowron, Andrzej
TI - A rough set-based knowledge discovery process
JO - International Journal of Applied Mathematics and Computer Science
PY - 2001
VL - 11
IS - 3
SP - 603
EP - 619
AB - 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.
LA - eng
KW - KDD process; rough sets; hybrid systems; knowledge discovery; real-life databases
UR - http://eudml.org/doc/207522
ER -

References

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