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ID 28454
本文ファイル
Thumnail A1202.pdf 270 KB
著者
Yeh, Chi-Yuan
Ouyang, Jeng
Lee, Shie-Jue
キーワード
Large-scale dataset
fuzzy similarity
data reduction
prototype reduction
instance-filtering
instance-abstraction
NDC
技術・工学
抄録(英)
Finding an efficient data reduction method for largescale 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.
掲載誌名
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
開始ページ
65
終了ページ
70
出版年月日
2009-11
出版者
IEEE SMC Hiroshima Chapter
ISSN
1883-3977
言語
英語
NII資源タイプ
会議発表論文
広大資料タイプ
会議発表論文
DCMIタイプ
text
フォーマット
application/pdf
著者版フラグ
publisher
権利情報
(c) Copyright by IEEE SMC Hiroshima Chapter.
関連情報URL
部局名
工学研究科