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 a new multi-level algorithm based on particle swarm optimization (PSO) for bisecting graph. The success of our algorithm relies on exploiting both the PSO 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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Sun, L., Leng, M., Yu, S. (2007). A New Multi-level Algorithm Based on Particle Swarm Optimization for Bisecting Graph. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_8
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DOI: https://doi.org/10.1007/978-3-540-73871-8_8
Publisher Name: Springer, Berlin, Heidelberg
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