Discretization algorithms of rough sets using clustering

Chengdong Wu; Mengxin Li; Zhonghua Han; Ying Zhang; Yong Yue

Discretization algorithms of rough sets using clustering

Chengdong Wu^*, Mengxin Li, Zhonghua Han, Ying Zhang, Yong Yue

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

In this paper, hierarchical clustering method is introduced for attribute discretization. It can determine automatically the significant clusters. First the best classes for discretization are picked from scatter plots of several statistics. Moreover these classes keep consistent with extracted clusters from dendrograms. By comparison, hierarchical clustering discretiztion method is typically more effective and advisable among several cluster algorithms with the defect inspection of wood veneer.

Original language	English
Title of host publication	Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004
Pages	955-960
Number of pages	6
Publication status	Published - 2004
Externally published	Yes
Event	Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004 - Shenyang, China Duration: 22 Aug 2004 → 26 Aug 2004

Publication series

Name	Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004

Conference

Conference	Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004
Country/Territory	China
City	Shenyang
Period	22/08/04 → 26/08/04

Keywords

Attribute discretization
Dendrogram
Hierarchical clustering
Rough set theory

Cite this

@inproceedings{a7686c9a4dd54aa4b557b93ecee2eef5,

title = "Discretization algorithms of rough sets using clustering",

abstract = "In this paper, hierarchical clustering method is introduced for attribute discretization. It can determine automatically the significant clusters. First the best classes for discretization are picked from scatter plots of several statistics. Moreover these classes keep consistent with extracted clusters from dendrograms. By comparison, hierarchical clustering discretiztion method is typically more effective and advisable among several cluster algorithms with the defect inspection of wood veneer.",

keywords = "Attribute discretization, Dendrogram, Hierarchical clustering, Rough set theory",

author = "Chengdong Wu and Mengxin Li and Zhonghua Han and Ying Zhang and Yong Yue",

year = "2004",

language = "English",

isbn = "0780386418",

series = "Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004",

pages = "955--960",

booktitle = "Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004",

note = "Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004 ; Conference date: 22-08-2004 Through 26-08-2004",

}

Wu, C, Li, M, Han, Z, Zhang, Y & Yue, Y 2004, Discretization algorithms of rough sets using clustering. in Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004. Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004, pp. 955-960, Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004, Shenyang, China, 22/08/04.

Discretization algorithms of rough sets using clustering. / Wu, Chengdong; Li, Mengxin; Han, Zhonghua et al.
Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004. 2004. p. 955-960 (Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Discretization algorithms of rough sets using clustering

AU - Wu, Chengdong

AU - Li, Mengxin

AU - Han, Zhonghua

AU - Zhang, Ying

AU - Yue, Yong

PY - 2004

Y1 - 2004

N2 - In this paper, hierarchical clustering method is introduced for attribute discretization. It can determine automatically the significant clusters. First the best classes for discretization are picked from scatter plots of several statistics. Moreover these classes keep consistent with extracted clusters from dendrograms. By comparison, hierarchical clustering discretiztion method is typically more effective and advisable among several cluster algorithms with the defect inspection of wood veneer.

AB - In this paper, hierarchical clustering method is introduced for attribute discretization. It can determine automatically the significant clusters. First the best classes for discretization are picked from scatter plots of several statistics. Moreover these classes keep consistent with extracted clusters from dendrograms. By comparison, hierarchical clustering discretiztion method is typically more effective and advisable among several cluster algorithms with the defect inspection of wood veneer.

KW - Attribute discretization

KW - Dendrogram

KW - Hierarchical clustering

KW - Rough set theory

UR - http://www.scopus.com/inward/record.url?scp=28344450448&partnerID=8YFLogxK

M3 - Conference Proceeding

AN - SCOPUS:28344450448

SN - 0780386418

T3 - Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004

SP - 955

EP - 960

BT - Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004

T2 - Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004

Y2 - 22 August 2004 through 26 August 2004

ER -

Discretization algorithms of rough sets using clustering

Abstract

Publication series

Conference

Keywords

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