A Clustering-Based Algorithm for Data Reduction

Yeh Chi-Yuan

Ouyang Jeng

Lee Shie-Jue

5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009

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.

Large-scale dataset

fuzzy similarity

data reduction

prototype reduction

instance-filtering

instance-abstraction

技術・工学 [ 500 ]

英語

IEEE SMC Hiroshima Chapter

(c) Copyright by IEEE SMC Hiroshima Chapter.

[ISSN] 1883-3977

[URI] http://www.hil.hiroshima-u.ac.jp/iwcia/2009/