Rough K-means cluster with adaptive parameters

Tao Zhou; Yan Ning Zhang; H. E.Jing Yuan; Hui Ling Lu

doi:10.1109/ICMLC.2007.4370674

Rough K-means cluster with adaptive parameters

Tao Zhou, Yan Ning Zhang, H. E.Jing Yuan, Hui Ling Lu

School of Computer Science

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

7 Scopus citations

Abstract

In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the clusters. Then we point out its shortcoming in adjusting the three coefficients W_l , W_u and ∈. To tackle this problem, a rough k-means clustering method is Anally presented with adaptive parameters. This algorithm is used in a testing sample and obtains a less error clustering rate.

Original language	English
Title of host publication	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages	3063-3068
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2007.4370674
State	Published - 2007
Event	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China Duration: 19 Aug 2007 → 22 Aug 2007

Publication series

Name	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume	6

Conference

Conference	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/Territory	China
City	Hong Kong
Period	19/08/07 → 22/08/07

Keywords

Adaptive parameters
Clustering algorithm
Rough k-means
Rough sets

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10.1109/ICMLC.2007.4370674

Cite this

Zhou, T., Zhang, Y. N., Yuan, H. E. J., & Lu, H. L. (2007). Rough K-means cluster with adaptive parameters. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 (pp. 3063-3068). Article 4370674 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 6). https://doi.org/10.1109/ICMLC.2007.4370674

@inproceedings{7fcfd9680b754849b0f1c02016246783,

title = "Rough K-means cluster with adaptive parameters",

abstract = "In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the clusters. Then we point out its shortcoming in adjusting the three coefficients Wl , Wu and ∈. To tackle this problem, a rough k-means clustering method is Anally presented with adaptive parameters. This algorithm is used in a testing sample and obtains a less error clustering rate.",

keywords = "Adaptive parameters, Clustering algorithm, Rough k-means, Rough sets",

author = "Tao Zhou and Zhang, {Yan Ning} and Yuan, {H. E.Jing} and Lu, {Hui Ling}",

year = "2007",

doi = "10.1109/ICMLC.2007.4370674",

language = "英语",

isbn = "142440973X",

series = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

pages = "3063--3068",

booktitle = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

note = "6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 ; Conference date: 19-08-2007 Through 22-08-2007",

}

Zhou, T, Zhang, YN, Yuan, HEJ & Lu, HL 2007, Rough K-means cluster with adaptive parameters. in Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007., 4370674, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007, vol. 6, pp. 3063-3068, 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007, Hong Kong, China, 19/08/07. https://doi.org/10.1109/ICMLC.2007.4370674

Rough K-means cluster with adaptive parameters. / Zhou, Tao; Zhang, Yan Ning; Yuan, H. E.Jing et al.
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 3063-3068 4370674 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 6).

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

TY - GEN

T1 - Rough K-means cluster with adaptive parameters

AU - Zhou, Tao

AU - Zhang, Yan Ning

AU - Yuan, H. E.Jing

AU - Lu, Hui Ling

PY - 2007

Y1 - 2007

N2 - In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the clusters. Then we point out its shortcoming in adjusting the three coefficients Wl , Wu and ∈. To tackle this problem, a rough k-means clustering method is Anally presented with adaptive parameters. This algorithm is used in a testing sample and obtains a less error clustering rate.

AB - In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the clusters. Then we point out its shortcoming in adjusting the three coefficients Wl , Wu and ∈. To tackle this problem, a rough k-means clustering method is Anally presented with adaptive parameters. This algorithm is used in a testing sample and obtains a less error clustering rate.

KW - Adaptive parameters

KW - Clustering algorithm

KW - Rough k-means

KW - Rough sets

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U2 - 10.1109/ICMLC.2007.4370674

DO - 10.1109/ICMLC.2007.4370674

M3 - 会议稿件

AN - SCOPUS:38049064484

SN - 142440973X

SN - 9781424409730

T3 - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

SP - 3063

EP - 3068

BT - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

T2 - 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007

Y2 - 19 August 2007 through 22 August 2007

ER -

Rough K-means cluster with adaptive parameters

Abstract

Publication series

Conference

Keywords

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