# A rough set-based knowledge discovery process

International Journal of Applied Mathematics and Computer Science (2001)

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

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topZhong, 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 -

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