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ID 19639
Eprint ID
19639
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
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.
キーワード
Large-scale dataset
fuzzy similarity
data reduction
prototype reduction
instance-filtering
instance-abstraction
発行日
2009-11-12
出版物タイトル
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
2009巻
1号
出版者
IEEE SMC Hiroshima Chapter
開始ページ
65
終了ページ
70
ISSN
1883-3977
NCID
BB00577064
資料タイプ
会議発表論文
言語
English
著作権者
IEEE SMC Hiroshima Chapter
イベント
5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
イベント地
東広島市
イベント地の別言語
Higashi-Hiroshima City
論文のバージョン
publisher
査読
有り
Eprints Journal Name
IWCIA