Abstract
An important application of graph partitioning is data clustering using a graph model — the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. 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 based on ant colony optimization(ACO) for bisecting graph. The success of our algorithm relies on exploiting both the ACO method and the concept of the graph core. Our experimental evaluations on 18 different graphs show that our algorithm produces encouraging 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. (2007). An Effective Multi-level Algorithm Based on Ant Colony Optimization for Bisecting Graph. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_16
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DOI: https://doi.org/10.1007/978-3-540-71701-0_16
Publisher Name: Springer, Berlin, Heidelberg
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