Regular ArticleParallel Algorithms for Hierarchical Clustering and Applications to Split Decomposition and Parity Graph Recognition
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2019, Computer Physics CommunicationsCitation Excerpt :All code can be found online at https://github.com/openerror/PhysicsLatticeClustering. There are many implementations of hierarchical clustering [13,14], some of which are parallelized [15–17]. In this paper we will work with the agglomerative variant, which forms clusters from the bottom-up — i.e. starting from single observations [4].
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2019, Expert Systems with ApplicationsCitation Excerpt :For hierarchical clustering, Koga, Ishibashi, and Watanabe (2007) develop a method for agglomerative clustering that approximates the process of finding the nearest neighbor using hashing for choosing items to be merged, reducing the complexity of the algorithm. In addition, there have been several papers on how to best parallelize agglomerative clustering algorithms (Dahlhaus, 2000; Li, 1990; Olson, 1995). Similar methods exist for k-means clustering.
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Present address: Institute of Computer Graphics, Vienna University of Technology.