Abstract
Clustering is an important approach to graph partitioning. In this process a graph model expressed as the pairwise similarities between all data objects is represented as a weighted graph adjacency matrix. The min-cut bipartitioning problem is a fundamental graph partitioning problem and is NP-Complete. In this paper, we present an effective multi-level algorithm for bisecting graph. The success of our algorithm relies on exploiting both Tabu search theory and the concept of the graph core. Our experimental evaluations on 18 different graphs show that our algorithm produces excellent solutions compared with those produced by MeTiS that is a state-of-the-art partitioner in the literature.
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Leng, M., Yu, S. (2006). An Effective Multi-level Algorithm for Bisecting Graph. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_54
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DOI: https://doi.org/10.1007/11811305_54
Publisher Name: Springer, Berlin, Heidelberg
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