Correlation Clustering in MapReduce

author: Flavio Chierichetti, Dipartimento di Informatica, Sapienza University of Rome
published: Oct. 7, 2014,   recorded: August 2014,   views: 2626


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: