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ICPR
2008
IEEE

Geodesic K-means clustering

15 years 11 days ago
Geodesic K-means clustering
We introduce a class of geodesic distances and extend the K-means clustering algorithm to employ this distance metric. Empirically, we demonstrate that our geodesic K-means algorithm exhibits several desirable characteristics missing in the classical K-means. These include adjusting to varying densities of clusters, high levels of resistance to outliers, and handling clusters that are not linearly separable. Furthermore our comparative experiments show that geodesic K-means comes very close to competing with state-of-the-art algorithms such as spectral and hierarchical clustering.
Arian Maleki, Nima Asgharbeygi
Added 05 Nov 2009
Updated 05 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Arian Maleki, Nima Asgharbeygi
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