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Distributed Balanced Clustering via Mapping Coresets
Mohammadhossein Bateni · Aditya Bhaskara · Silvio Lattanzi · Vahab Mirrokni

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D #None

Large-scale clustering of data points in metric spaces is an important problem in mining big data sets. For many applications, we face explicit or implicit size constraints for each cluster which leads to the problem of clustering under capacity constraints or the balanced clustering'' problem. Although the balanced clustering problem has been widely studied, developing a theoretically sound distributed algorithm remains an open problem. In the present paper we develop a general framework based onmapping coresets'' to tackle this issue. For a wide range of clustering objective functions such as k-center, k-median, and k-means, our techniques give distributed algorithms for balanced clustering that match the best known single machine approximation ratios.

Author Information

Mohammadhossein Bateni (Google research)
Aditya Bhaskara (University of Utah)
Silvio Lattanzi (Google Research)
Vahab Mirrokni (Google Research NYC)

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