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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ming Liu Yuan-Chao Liu Xiao-Long Wang |
| Copyright Year | 2008 |
| Description | Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin (Ming Liu; Yuan-Chao Liu; Xiao-Long Wang) |
| Abstract | Because of today's explosive information from Internet, people will contact much new information at any moment. So how to analyze this non-stationary information becomes more and more important. Clustering analysis is a good information analysis method, but many clustering algorithms only fit to stationary situation. Then in this paper, a novel incremental clustering algorithm based on self-organizing-mapping-IGSOM is provided to dispose this non-stationary information. This algorithm first uses self-organizing-mapping algorithm to construct a neuron model from original data. Then it selects some sample data from this neuron model, and combines the samples with new coming data together to train a new neuron model. To solve unbalance between sample data and new coming data, it alters sample data's weights. The experiments demonstrate that this incremental clustering method can dispose non-stationary data well, and has relatively high precision. Because only small samples are selected to replace large-scale original data, clustering time is also short. |
| Starting Page | 885 |
| Ending Page | 890 |
| File Size | 279917 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769532783 |
| DOI | 10.1109/IIH-MSP.2008.101 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-08-15 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Algorithm design and analysis incremental clustering Neurons Clustering algorithms Signal processing algorithms Data models sample data selection self organizing mapping Testing |
| Content Type | Text |
| Resource Type | Article |
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