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Poster
Sliding Window Algorithms for k-Clustering Problems
Michele Borassi · Alessandro Epasto · Silvio Lattanzi · Sergei Vassilvitskii · Morteza Zadimoghaddam

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1085
in Poster Session 4 »
The sliding window model of computation captures scenarios in which data is arriving continuously, but only the latest $w$ elements should be used for analysis. The goal is to design algorithms that update the solution efficiently with each arrival rather than recomputing it from scratch. In this work, we focus on $k$-clustering problems such as $k$-means and $k$-median. In this setting, we provide simple and practical algorithms that offer stronger performance guarantees than previous results. Empirically, we show that our methods store only a small fraction of the data, are orders of magnitude faster, and find solutions with costs only slightly higher than those returned by algorithms with access to the full dataset.

Author Information

Michele Borassi (Google Switzerland GmbH)
Alessandro Epasto (Google)
Alessandro Epasto

I am a staff research scientist at Google, New York working in the Google Research Algorithms and Optimization team lead by Vahab Mirrokni. I received a Ph.D in computer science from Sapienza University of Rome, where I was advised by Professor Alessandro Panconesi and supported by the Google Europe Ph.D. Fellowship in Algorithms, 2011. I was also a post-doc at the department of computer science of Brown University in Providence (RI), USA where I was advised by Professor Eli Upfal. My research interests include algorithmic problems in machine learning and data mining, in particular in the areas of clustering, privacy, and large scale graphs analysis.

Silvio Lattanzi (Google Research)
Sergei Vassilvitskii (Google)
Morteza Zadimoghaddam (Google Research)

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