Abstract
This work proposes a load balance algorithm to parallel processing based on a variation of the classical knapsack problem. The problem considers the distribution of a set of partitions, defined by the number of clusters, over a set of processors attempting to achieve a minimal overall processing cost.
The work is an optimization for the parallel fuzzy c-means (FCM) clustering analysis algorithm proposed in a previous work composed by two distinct parts: the cluster analysis, properly said, using the FCM algorithm to calculate of clusters centers and the PBM index to evaluate partitions, and the load balance, which is modeled by the multiple knapsack problem and implemented through a heuristic that incorporates the restrictions related to cluster analysis in order to gives more efficiency to the parallel process.
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Modenesi, M.V., Evsukoff, A.G., Costa, M.C.A. (2008). A Load Balancing Knapsack Algorithm for Parallel Fuzzy c-Means Cluster Analysis. In: Palma, J.M.L.M., Amestoy, P.R., Daydé, M., Mattoso, M., Lopes, J.C. (eds) High Performance Computing for Computational Science - VECPAR 2008. VECPAR 2008. Lecture Notes in Computer Science, vol 5336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92859-1_24
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DOI: https://doi.org/10.1007/978-3-540-92859-1_24
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