2014 CorrelationClusteringinMapReduc

• (Chierichetti et al., 2014) ⇒ Flavio Chierichetti, Nilesh Dalvi, and Ravi Kumar. (2014). “Correlation Clustering in MapReduce.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623743

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Correlation+Clustering+in+MapReduce
• http://dl.acm.org/citation.cfm?id=2623330.2623743&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; scalable clustering; signed networks.

Abstract

Correlation clustering is a basic primitive in data miner's toolkit with applications ranging from entity matching to social network analysis. The goal in correlation clustering is, given a graph with signed edges, partition the nodes into clusters to minimize the number of disagreements. In this paper we obtain a new algorithm for correlation clustering. Our algorithm is easily implementable in computational models such as MapReduce and streaming, and runs in a small number of rounds. In addition, we show that our algorithm obtains an almost 3-approximation to the optimal correlation clustering. Experiments on huge graphs demonstrate the scalability of our algorithm and its applicability to data mining problems.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 CorrelationClusteringinMapReduc	Flavio Chierichetti Ravi Kumar Nilesh Dalvi			Correlation Clustering in MapReduce				10.1145/2623330.2623743		2014