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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0907109-164128 |
| Date | 07 September 2009 |
| Creators | Ouyang, Jeng |
| Contributors | Chen-sen Ouyang, Chih-chin Lai, Hsien-leing Tsai, Chih-hung Wu, johnw@nuk.edu.tw |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0907109-164128 |
| Rights | unrestricted, Copyright information available at source archive |
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