このエントリーをはてなブックマークに追加
ID 28454
file
A1202.pdf 270 KB
creator
Yeh, Chi-Yuan
Ouyang, Jeng
Lee, Shie-Jue
subject
Large-scale dataset
fuzzy similarity
data reduction
prototype reduction
instance-filtering
instance-abstraction
NDC
Technology. Engineering
abstract
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.
journal title
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
start page
65
end page
70
date of issued
2009-11
publisher
IEEE SMC Hiroshima Chapter
issn
1883-3977
language
eng
nii type
Conference Paper
HU type
Conference Papers
DCMI type
text
format
application/pdf
text version
publisher
rights
(c) Copyright by IEEE SMC Hiroshima Chapter.
relation url
department
Graduate School of Engineering