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| 論文名稱 | A genetic clustering algorithm for data with non-spherical-shape clusters |
| 發表日期 | 2000-12-01 |
| 論文收錄分類 | SCI |
| 所有作者 | Lin Yu Tseng, Shiueng Bien Yang |
| 作者順序 | 第二作者 |
| 通訊作者 | 否 |
| 刊物名稱 | pattern recognition |
| 發表卷數 | 33 |
| 是否具有審稿制度 | 是 |
| 發表期數 | 7 |
| 期刊或學報出版地國別/地區 | NATTWN-中華民國 |
| 發表年份 | 2000 |
| 發表月份 | 12 |
| 發表形式 | 電子期刊 |
| 所屬計劃案 | 無 |
| 可公開文檔 |
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| 可公開文檔 |
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| 可公開文檔 | |
| 附件 |
 Agenetic03112411002426740.pdf
|
[英文摘要] :
In solving clustering problem, traditional methods, for example, the K-means algorithm and its variants, usually ask
the user to provide the number of clusters. Unfortunately, the number of clusters in general is unknown to the user. The
traditional neighborhood clustering algorithm usually needs the user to provide a distance d for the clustering. This d is
di$cult to decide because some clusters may be compact but others may be loose. In this paper, we propose a genetic
clustering algorithm for clustering the data whose clusters are not of spherical shape. It can automatically cluster the data
according to the similarities and automatically "nd the proper number of clusters. The experimental results are given to
illustrate the e!ectiveness of the genetic algorithm. ( 2000 Pattern Recognition Society. Published by Elsevier Science
Ltd. All rights reserved.
[參考文獻] :
[1] M.R. Anderberg, Cluster Analysis for Applications, Academic
Press, New York, 1973.
[2] J.T. Tou, R.C. Gonzalez, Pattern Recognition Principles,
Addision-Wesley, Reading, MA, 1974.
[3] J.A. Hartigan, Clustering Algorithms, Wiley, New York,
1975.
[4] K.S. Fu, Communication and Cybernetics: Digital Pattern
Recognition, Springer, Berlin, 1980.
[5] R. Dubes, A.K. Jain, Clustering Methodology in Exploratory
Data Analysis, Academic Press, New York,
1980.
[6] P.A. Devijver, J. Kittler, Pattern Recognition-A Statistical
Approach, Prentice-Hall, London, 1982.
[7] S.Z. Selim, M.A. Ismail, K-means-type algorithm: generalized
convergence theorem and characterization of local
optimality, IEEE Trans. Pattern Anal. Mach. Intell. 6 (1)
(1984) 81}87.
1258 L.Y. Tseng, S.B. Yang / Pattern Recognition 33 (2000) 1251}1259
[8] W.I. Koontz, P.M. Narendra, K. Fukunaga, A branch and
bound clustering algorithm, IEEE Trans. Comput. c- 24 (9)
(1975) 908}915.
[9] S.Z. Selim, K.S. Al-Sultan, A simulated annealing algorithm
for the clustering problem, Pattern Recognition 24
(10) (1991) 1003}1008.
[10] R.W. Klein, R.C. Dubes, Experiments in projection and
clustering by simulated annealing, Pattern Recognition 22
(2) (1989) 213}220.
[11] P.-Y. Yin, L.-H. Chen, A new non-iterative approach for
clustering, Pattern Recognition Letters 15 (2) (1994)
125}133.
[12] G.C. Osbourn, R.F. Martinez, Empirically de"ned regions
of in#uence for clustering analyses, Pattern Recognition 28
(11) (1995) 1793}1806.
[13] P.S. Stephen, Threshold validity for mutual neighborhood
clustering, IEEE Trans. Pattern Anal. March. Intell. 15 (1)
(1993) 89}92.
