A rough set approach to attribute generalization in data mining

被引：105

作者：

Chan, CC ^{[1
]}

机构：

[1] Univ Akron, Dept Math Sci, Akron, OH 44325 USA

来源：

INFORMATION SCIENCES | 1998年 / 107卷 / 1-4期

关键词：

rough sets; data mining; inductive learning;

D O I：

10.1016/S0020-0255(97)10047-0

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification rules from very large data bases generalized by dynamic conceptual hierarchies provided by users. In general, the process of attribute generalization may introduce inconsistency into a generalized relation. This issue is resolved by using the inductive learning algorithm, LERS based on rough set theory. (C) 1998 Elsevier Science Inc. All rights reserved.

引用

页码：169 / 176

页数：8

共 9 条

[1]

[Anonymous], P 4 INT S METH INT S

[2]

[Anonymous], 23 ANN COMP SCI WORK

[3]

[Anonymous], 1991, MANAGING UNCERTAINTY

[4]

[Anonymous], ADV KNOWLEDGE DISCOV

[5]

Cai Y., 1991, Knowledge discovery in databases, P213

[6] INCREMENTAL LEARNING OF PRODUCTION RULES FROM EXAMPLES UNDER UNCERTAINTY - A ROUGH SET APPROACH [J].

CHAN, CC .

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 1991, 1 (04) :439-461

[7]

Grzymala-Busse J. W., 1988, Journal of Intelligent and Robotic Systems: Theory and Applications, V1, P3, DOI 10.1007/BF00437317

[8]

GRZYMALABUSSE JW, 1991, P 3 MIDW ART INT COG, P103

[9] ROUGH SETS [J].

PAWLAK, Z .

INTERNATIONAL JOURNAL OF COMPUTER & INFORMATION SCIENCES, 1982, 11 (05) :341-356

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