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Mercer kernel-based clustering in feature space

โœ Scribed by Girolami, M.


Book ID
120461609
Publisher
IEEE
Year
2002
Tongue
English
Weight
363 KB
Volume
13
Category
Article
ISSN
1045-9227

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By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in uns