ID | 19639 |
Eprint ID | 19639
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フルテキストURL | |
著者 |
Yeh Chi-Yuan
National Sun Yat-Sen University
Ouyang Jeng
National Sun Yat-Sen University
Lee Shie-Jue
National Sun Yat-Sen University
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抄録 | Finding an efficient data reduction method for large-scale
problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automatically. Then the statistical mean for each cluster will be regarded as representing all the instances covered in the cluster. This approach has two advantages. One is that it can be faster and uses less storage memory. The other is that the number of new representative instances need not be specified in advance by the user. Experiments on real-world datasets show that our method can run faster and obtain better reduction rate than other methods.
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キーワード | Large-scale dataset
fuzzy similarity
data reduction
prototype reduction
instance-filtering
instance-abstraction
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発行日 | 2009-11-12
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出版物タイトル |
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
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巻 | 2009巻
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号 | 1号
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出版者 | IEEE SMC Hiroshima Chapter
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開始ページ | 65
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終了ページ | 70
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ISSN | 1883-3977
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NCID | BB00577064
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資料タイプ |
会議発表論文
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言語 |
英語
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著作権者 | IEEE SMC Hiroshima Chapter
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イベント | 5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
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イベント地 | 東広島市
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イベント地の別言語 | Higashi-Hiroshima City
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論文のバージョン | publisher
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査読 |
有り
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Eprints Journal Name | IWCIA
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