To make effective use of distributed information, it is desirable to allow coordination and collaboration among various information sources. This paper deals with clustering data emanating from different sites. The process of clustering consists of three steps: find the (local) clusters of data at each site; find (higher) clusters from the union of the distributed data sets at the central site;...

Clustering large datasets is a fundamental problem with a number of applications in machine learning. Data is often collected on different sites and clustering needs to be performed in a distributed manner with low communication. We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single server. In t...

This paper provides new algorithms for distributed clustering for two popular center-based objec-tives, k-median and k-means. These algorithms have provable guarantees and improve communicationcomplexity over existing approaches. Following a classic approach in clustering by [13], we reduce theproblem of finding a clustering with low cost to the problem of finding a ‘coreset’ of...