Abstract
Hierarchical clustering methods are important in many data mining and pattern recognition tasks. In this paper we present an efficient coarse grained parallel algorithm for Single Link Clustering; a standard inter-cluster linkage metric. Our approach is to first describe algorithms for the Prefix Larger Integer Set and the Closest Larger Ancestor problems and then to show how these can be applied to solve the Single Link Clustering problem. In an extensive performance analysis an implementation of these algorithms on a Linux-based cluster has shown to scale well, exhibiting near linear relative speedup.
Research partially supported by the Natural Sciences and Engineering Research Council of Canada
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Chan, A., Gao, C., Rau-Chaplin, A. (2005). A Coarse Grained Parallel Algorithm for Closest Larger Ancestors in Trees with Applications to Single Link Clustering. In: Yang, L.T., Rana, O.F., Di Martino, B., Dongarra, J. (eds) High Performance Computing and Communications. HPCC 2005. Lecture Notes in Computer Science, vol 3726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557654_96
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DOI: https://doi.org/10.1007/11557654_96
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