Clustering
Efficient Centroid-Linkage Clustering
We also evaluate our algorithm empirically. By leveraging a state-of-the-art nearest-neighbor search library, we obtain a fast and accurate Centroid-Linkage HAC algorithm.
Variance Reduction in Bipartite Experiments through Correlation Clustering
Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni
Neural Information Processing Systems http://nips.cc/
dist(x,y) andavg(A,B) = 1 |A| |B| X
In this paper, we present a comprehensive study of the performance of average-link in metric spaces, regarding several natural criteria that capture separability and cohesion, and aremore interpretable than Dasgupta'scost function and itsvariants.
Ultrametric Fitting by Gradient Descent
Giovanni Chierchia, Benjamin Perret
We study the problem of fitting an ultrametric distance to a dissimilarity graph in the context of hierarchical cluster analysis.
How to tell when a clustering is (approximately) correct using convex relaxations
Neural Information Processing Systems http://nips.cc/
Foundations of Comparison-Based Hierarchical Clustering
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Neural Information Processing Systems http://nips.cc/
