Poster

Online and Consistent Correlation Clustering

Vincent Cohen-Addad · Silvio Lattanzi · Andreas Maggiori · Nikos Parotsidis

Hall E #611

Keywords: [ T: Optimization ] [ MISC: Unsupervised and Semi-supervised Learning ] [ OPT: Discrete and Combinatorial Optimization ]


Abstract:

In the correlation clustering problem the input is a signed graph where the sign indicates whether each pair of points should be placed in the same cluster or not. The goal of the problem is to compute a clustering which minimizes the number of disagreements with such recommendation. Thanks to its many practical applications, correlation clustering is a fundamental unsupervised learning problem and has been extensively studied in many different settings. In this paper we study the problem in the classic online setting with recourse; The vertices of the graphs arrive in an online manner and the goal is to maintain an approximate clustering while minimizing the number of times each vertex changes cluster. Our main contribution is an algorithm that achieves logarithmic recourse per vertex in the worst case. We also complement this result with a tight lower bound. Finally we show experimentally that our algorithm achieves better performances than state-of-the-art algorithms on real world data.

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