東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Rough set-based approach to data mining.

Guo, Jia-Yuarn.

Linked to FindBook

Google Book

Amazon

博客來

Rough set-based approach to data mining.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Rough set-based approach to data mining./
Author:	Guo, Jia-Yuarn.
Description:	267 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1469.
Contained By:	Dissertation Abstracts International64-03B.
Subject:	Engineering, System Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3085427

Rough set-based approach to data mining.
Guo, Jia-Yuarn.

Rough set-based approach to data mining. - 267 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1469.

Thesis (Ph.D.)--Case Western Reserve University, 2003.

To improve competitiveness, enterprises—big or small—have been trying to use information technology to help in all aspects of their business. The growing volume of data in digital form and advances in data analysis methodologies and information technology have led to a field that attempts to extract useful information and intelligence from these large data sets for the purpose of strategic and decision making, such a rapidly growing field is called Data Mining.Subjects--Topical Terms:

1018128
Engineering, System Science.

Rough set-based approach to data mining.
LDR:02674nmm 2200277 4500 001 1852862
005 20040615083618.5
008 130614s2003 eng d
035 $a (UnM)AAI3085427
035 $a AAI3085427
040 $a UnM $c UnM
100 1 $a Guo, Jia-Yuarn. $3 1940750
245 1 0 $a Rough set-based approach to data mining.
300 $a 267 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1469.
500 $a Adviser: Vira Chankong.
502 $a Thesis (Ph.D.)--Case Western Reserve University, 2003.
520 $a To improve competitiveness, enterprises—big or small—have been trying to use information technology to help in all aspects of their business. The growing volume of data in digital form and advances in data analysis methodologies and information technology have led to a field that attempts to extract useful information and intelligence from these large data sets for the purpose of strategic and decision making, such a rapidly growing field is called Data Mining.
520 $a This dissertation focuses on feature extraction and rule induction aspects of data mining based on the so-called Rough Set theory. The first aim of this dissertation is to develop an optimisation feature extraction model, which can reduce the unnecessary input variables by selecting the desired and representative reducts from each object. The second main purpose is to develop a rule-generation algorithm to generate a minimal set of rule-reduct, from which useful knowledge can be induced. Finally, we want to modify the rule-generation to apply to incomplete information systems. The organization of this dissertation is as follows. Chapter 1 discuss the motivation and dissertation outline. Chapter 2 introduces basic theories and applications of Rough sets, feature extraction model and rule-generation algorithm of literature review. Chapter 3 presents the integer programming model for feature extraction problems. Chapter 4 proposes rule-generation algorithm and rule induction for complete information systems. Chapter 5 proposes a generalized rule-generation algorithm and rule induction for incomplete information systems. Chapter 6 presents numerical results. We demonstrate the test results of using examples found in the literature as well as newly constructed. Chapter 7 presents the conclusion and future work that we plan to carry out.
590 $a School code: 0042.
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Computer Science. $3 626642
690 $a 0790
690 $a 0984
710 2 0 $a Case Western Reserve University. $3 1017714
773 0 $t Dissertation Abstracts International $g 64-03B.
790 1 0 $a Chankong, Vira, $e advisor
790 $a 0042
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3085427