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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kong Dexi Kong Rui |
| Copyright Year | 2013 |
| Description | Author affiliation: Jinan Univ., Guangzhou, China (Kong Dexi; Kong Rui) |
| Abstract | In the paper, we applied the idea of kernel-based learning methods to K-means clustering. We propose a fast and effective algorithm of kernel K-means clustering. The idea of the algorithm is that we firstly map the data from their original space to a high dimensional space (or kernel space) where the data are expected to be more separable. Then we perform K-means clustering in the high dimensional kernel space. Meanwhile we improve speed of the algorithm by using a new kernel function-conditionally positive definite kernel (CPD). The performance of new algorithm has been demonstrated to be superior to that of K-means clustering algorithm by our experiments on artificial and real data. |
| Starting Page | 58 |
| Ending Page | 61 |
| File Size | 197925 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467348935 |
| DOI | 10.1109/ISDEA.2012.21 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-01-16 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines K-Means Clustering Clustering algorithms Machine learning Educational institutions Classification algorithms Kernel K-Means Clustering Kernel Support Vector Machines Unsupervised learning Kernel Function |
| Content Type | Text |
| Resource Type | Article |
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