Improved algorithms for the random cluster graph model

Ron Shamir, Dekal Tsur

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


We model noisy clustering data using random graphs: Clusters correspond to disjoint sets of vertices. Two vertices from the same set (resp., different sets) share an edge with probability p (resp., r < p). We give algorithms that reconstruct the clusters from the graph with high probability. Compared to previous studies, our algorithms have lower time complexity and apply under wider parameter range.

Original languageEnglish
Pages (from-to)418-449
Number of pages32
JournalRandom Structures and Algorithms
Issue number4
StatePublished - 1 Dec 2007


  • Clustering
  • Planted partition

ASJC Scopus subject areas

  • Software
  • General Mathematics
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics


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