Abstract
Most graph-theoretical clustering algorithms require the complete adjacency relation of the graph representing the examined data. This is infeasible for very large graphs currently emerging in many application areas. We propose a local approach that computes clusters in graphs, one at a time, relying only on the neighborhoods of the vertices included in the current cluster candidate. This enables implementing a local and parameter-free algorithm. Approximate clusters may be identified quickly by heuristic methods. We report experimental results on clustering graphs using simulated annealing.
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Schaeffer, S.E. (2005). Stochastic Local Clustering for Massive Graphs. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_42
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DOI: https://doi.org/10.1007/11430919_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
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