Correlation clustering with stochastic labellings

Nicola Rebagliati, Samuel Rota Bulò, Marcello Pelillo

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

4 Citations (Scopus)


Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation graph in such a way as to minimize the disagreements in the cluster assignments. In this paper, we discuss a relaxation to the original problem setting which allows probabilistic assignments of vertices to labels. By so doing, overlapping clusters can be captured. We also show that a known optimization heuristic can be applied to the problem formulation, but with the automatic selection of the number of classes. Additionally, we propose a simple way of building an ensemble of agreement functions sampled from a reproducing kernel Hilbert space, which allows to apply correlation clustering without the empirical estimation of pairwise correlation values.
Original languageEnglish
Title of host publicationSimilarity-Based Pattern Recognition
Subtitle of host publicationSecond International Workshop SIMBAD 2013
EditorsEdwin Hancock, Marcello Pelillo
Place of PublicationBerlin - Heidelberg
ISBN (Electronic)978-3-642-39140-8
ISBN (Print)978-3-642-39139-2
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
Event2nd International Workshop on Similarity-Based Pattern Analysis and Recognition, SIMBAD 2013 - York, United Kingdom
Duration: 3 Jul 20135 Jul 2013

Publication series

SeriesLecture Notes in Computer Science


Conference2nd International Workshop on Similarity-Based Pattern Analysis and Recognition, SIMBAD 2013
Abbreviated titleSIMBAD 2013
Country/TerritoryUnited Kingdom


  • Baum-Eagon inequality
  • correlation clustering
  • ensemble clustering
  • stochastic labelling


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