Improved algorithms for the random cluster graph model

Ron Shamir, Dekel Tsur

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

10 Scopus citations

Abstract

The following probabilistic process models the generation of noisy clustering data: Clusters correspond to disjoint sets of vertices in a graph. Each two vertices from the same set are connected by an edge with probability p, and each two vertices from different sets are connected by an edge with probability r < p. The goal of the clustering problem is to reconstruct the clusters from the graph. We give algorithms that solve this problem with high probability. Compared to previous studies, our algorithms have lower time complexity and wider parameter range of applicability. In particular, our algorithms can handle O(√n/log n) clusters in an n-vertex graph, while all previous algorithms require that the number of clusters is constant.

Original languageEnglish
Title of host publicationAlgorithm Theory - SWAT 2002 - 8th Scandinavian Workshop on Algorithm Theory, Proceedings
EditorsMartti Penttonen, Erik Meineche Schmidt
PublisherSpringer Verlag
Pages230-239
Number of pages10
ISBN (Print)9783540438663
DOIs
StatePublished - 1 Jan 2002
Externally publishedYes
Event8th Scandinavian Workshop on Algorithm Theory, SWAT 2002 - Turku, Finland
Duration: 3 Jul 20025 Jul 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2368
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Scandinavian Workshop on Algorithm Theory, SWAT 2002
Country/TerritoryFinland
CityTurku
Period3/07/025/07/02

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