Online Correlation Clustering

Authors Claire Mathieu, Ocan Sankur, Warren Schudy

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Claire Mathieu
Ocan Sankur
Warren Schudy

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Claire Mathieu, Ocan Sankur, and Warren Schudy. Online Correlation Clustering. In 27th International Symposium on Theoretical Aspects of Computer Science. Leibniz International Proceedings in Informatics (LIPIcs), Volume 5, pp. 573-584, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)


We study the online clustering problem where data items arrive in an online fashion. The algorithm maintains a clustering of data items into similarity classes. Upon arrival of v, the relation between v and previously arrived items is revealed, so that for each u we are told whether v is similar to u. The algorithm can create a new luster for v and merge existing clusters. When the objective is to minimize disagreements between the clustering and the input, we prove that a natural greedy algorithm is O(n)-competitive, and this is optimal. When the objective is to maximize agreements between the clustering and the input, we prove that the greedy algorithm is .5-competitive; that no online algorithm can be better than .834-competitive; we prove that it is possible to get better than 1/2, by exhibiting a randomized algorithm with competitive ratio .5+c for a small positive fixed constant c.
  • Correlation clustering
  • online algorithms


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