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| 論文名稱 | A genetic approach to the automatic clustering problem |
| 發表日期 | 2001-12-01 |
| 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 | 34 |
| 是否具有審稿制度 | 是 |
| 發表期數 | 2 |
| 期刊或學報出版地國別/地區 | NATTWN-中華民國 |
| 發表年份 | 2001 |
| 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 |
 Agenetic03112411012727105.pdf
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[英文摘要] :
In solving the 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.
Therefore, clustering becomes a tedious trial-and-error work and the clustering result is often not very promising
especially when the number of clusters is large and not easy to guess. In this paper, we propose a genetic algorithm for the
clustering problem. This algorithm is suitable for clustering the data with compact spherical clusters. It can be used in
two ways. One is the user-controlled clustering, where the user may control the result of clustering by varying the values
of the parameter, w. A small value of w results in a larger number of compact clusters, while a large value of w results in
a smaller number of looser clusters. The other is an automatic clustering, where a heuristic strategy is applied to "nd
a good clustering. Experimental results are given to illustrate the e!ectiveness of this genetic clustering algorithm.
( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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