Iterative merging heuristics for correlation clustering

Andrzej Lingas, Mia Persson, Dzmitry Sledneu

Research output: Contribution to journalArticlepeer-review


A straightforward natural iterative heuristic for correlation clustering in the general setting is to start from singleton clusters and whenever merging two clusters improves the current quality score merge them into a single cluster. We analyse the approximation and complexity aspects of this heuristic and its three simple deterministic or random refinements.
Original languageEnglish
Pages (from-to)105-117
JournalInternational Journal of Metaheuristics
Issue number2
Publication statusPublished - 2014

Subject classification (UKÄ)

  • Computer Science

Free keywords

  • Randomised algorithms
  • Time complexity
  • Approximation algorithms
  • Correlation clustering
  • Graph clustering


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