Recent advances in data clustering concern clustering ensembles and projective clustering methods, each addressing different issues in clustering problems. In this paper, we consi...
Francesco Gullo, Carlotta Domeniconi, Andrea Tagar...
We give an implementation of the Goemans-Williamson clustering procedure which is at the core of several approximation algorithms including those for Generalized Steiner Trees, Pr...
Richard Cole, Ramesh Hariharan, Moshe Lewenstein, ...
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
Abstract. We present the first (to our knowledge) approximation algorithm for tensor clustering—a powerful generalization to basic 1D clustering. Tensors are increasingly common...
In this work we study the problem of Bipartite Correlation Clustering (BCC), a natural bipartite counterpart of the well studied Correlation Clustering (CC) problem. Given a bipart...
Nir Ailon, Noa Avigdor-Elgrabli, Edo Liberty, Anke...