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Improved algorithms for the random cluster graph model
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DOI：10.1002/rsa.20181.png)
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Abstract
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AbstractWe 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. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2007
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