Geodesic K-means clustering

  title={Geodesic K-means clustering},
  author={Nima Asgharbeygi and Arian Maleki},
  journal={2008 19th International Conference on Pattern Recognition},
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… CONTINUE READING
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