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13

ICPR
2008
IEEE

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Geodesic K-means clustering

14 years 5 months ago

Geodesic K-means clustering

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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

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Computer Vision | Geodesic K-means | ICPR 2008 | K-means Algorithm Exhibits | K-means Clustering Algorithm |

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Added	05 Nov 2009
Updated	05 Nov 2009
Type	Conference
Year	2008
Where	ICPR
Authors	Arian Maleki, Nima Asgharbeygi

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