Abstract
Recent innovations have enabled ever increasing amounts of data to be collected and stored, leading to the problem of extracting knowledge from it. Clustering techniques help organizing and understanding such data, and parallelization of such may reduce the cost of achieving this goal or improve on the result. This works presents the parallel implementation of the HACO clustering method, analyzing process of parallelization and its results with different topologies and communication strategies.
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Ramos, G.N. (2012). A Parallel Approach to Clustering with Ant Colony Optimization. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_11
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DOI: https://doi.org/10.1007/978-3-642-34459-6_11
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