distance computation
BanditPAM++: Faster k-medoids Clustering
Clustering is a fundamental task in data science with wide-ranging applications. In k-medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics may be used; these features allow for greater interpretability of the cluster centers and the clustering of exotic objects in k-medoids clustering, respectively.
BanditPAM++: Faster k-medoids Clustering
Clustering is a fundamental task in data science with wide-ranging applications. In k-medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics may be used; these features allow for greater interpretability of the cluster centers and the clustering of exotic objects in k-medoids clustering, respectively.
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves
Stefan Meintrup, Alexander Munteanu, Dennis Rohde
Neural Information Processing Systems http://nips.cc/
BanditPAM++: Faster k-medoids Clustering
Clustering is a fundamental task in data science with wide-ranging applications.
