A hierarchical clustering method for attribute discretization in rough set theory

Meng Xin Li; Cheng Dong Wu; Zhong Hua Han; Yong Yue

A hierarchical clustering method for attribute discretization in rough set theory

Meng Xin Li^*, Cheng Dong Wu, Zhong Hua Han, Yong Yue

^*Corresponding author for this work

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

20 Citations (Scopus)

Abstract

In this paper, a method, hierarchical clustering, is introduced that can determine automatically the significant clusters in a hierarchical cluster representation. It could choose best classes for discretization by scatter plots of several statistics primarily. Moreover we can extract the clusters from dendrograms that contain essentially the same information, which shows the two discretization results keep consistent. By comparison among several cluster algorithms with the defect inspection of wood veneer, hierarchical clustering discretiztion method is typically more effective and advisable.

Original language	English
Title of host publication	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages	3650-3654
Number of pages	5
Publication status	Published - 2004
Externally published	Yes
Event	Proceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: 26 Aug 2004 → 29 Aug 2004

Publication series

Name	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume	6

Conference

Conference	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Shanghai
Period	26/08/04 → 29/08/04

Keywords

Attribute discretization
Dendrogram
Hierarchical clustering
Rough set theory

Cite this

@inproceedings{3d85155b2da34c3faa85c520b8276a17,

title = "A hierarchical clustering method for attribute discretization in rough set theory",

abstract = "In this paper, a method, hierarchical clustering, is introduced that can determine automatically the significant clusters in a hierarchical cluster representation. It could choose best classes for discretization by scatter plots of several statistics primarily. Moreover we can extract the clusters from dendrograms that contain essentially the same information, which shows the two discretization results keep consistent. By comparison among several cluster algorithms with the defect inspection of wood veneer, hierarchical clustering discretiztion method is typically more effective and advisable.",

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

author = "Li, {Meng Xin} and Wu, {Cheng Dong} and Han, {Zhong Hua} and Yong Yue",

year = "2004",

language = "English",

isbn = "0780384032",

series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

pages = "3650--3654",

booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

note = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

}

Li, MX, Wu, CD, Han, ZH & Yue, Y 2004, A hierarchical clustering method for attribute discretization in rough set theory. in Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3650-3654, Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, 26/08/04.

A hierarchical clustering method for attribute discretization in rough set theory. / Li, Meng Xin; Wu, Cheng Dong; Han, Zhong Hua et al.
Proceedings of 2004 International Conference on Machine Learning and Cybernetics. 2004. p. 3650-3654 (Proceedings of 2004 International Conference on Machine Learning and Cybernetics; Vol. 6).

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

TY - GEN

T1 - A hierarchical clustering method for attribute discretization in rough set theory

AU - Li, Meng Xin

AU - Wu, Cheng Dong

AU - Han, Zhong Hua

AU - Yue, Yong

PY - 2004

Y1 - 2004

N2 - In this paper, a method, hierarchical clustering, is introduced that can determine automatically the significant clusters in a hierarchical cluster representation. It could choose best classes for discretization by scatter plots of several statistics primarily. Moreover we can extract the clusters from dendrograms that contain essentially the same information, which shows the two discretization results keep consistent. By comparison among several cluster algorithms with the defect inspection of wood veneer, hierarchical clustering discretiztion method is typically more effective and advisable.

AB - In this paper, a method, hierarchical clustering, is introduced that can determine automatically the significant clusters in a hierarchical cluster representation. It could choose best classes for discretization by scatter plots of several statistics primarily. Moreover we can extract the clusters from dendrograms that contain essentially the same information, which shows the two discretization results keep consistent. By comparison among several cluster algorithms with the defect inspection of wood veneer, hierarchical clustering discretiztion method is typically more effective and advisable.

KW - Attribute discretization

KW - Dendrogram

KW - Hierarchical clustering

KW - Rough set theory

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M3 - Conference Proceeding

AN - SCOPUS:6344243355

SN - 0780384032

T3 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

SP - 3650

EP - 3654

BT - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

T2 - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Y2 - 26 August 2004 through 29 August 2004

ER -

A hierarchical clustering method for attribute discretization in rough set theory

Abstract

Publication series

Conference

Keywords

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