Abstract
Many parallel and distributed clustering algorithms have already been proposed. Most of them are based on the aggregation of local models according to some collected local statistics. In this paper, we propose a lightweight distributed clustering algorithm based on minimum variance increases criterion which requires a very limited communication overhead. We also introduce the notion of distributed perturbation to improve the globally generated clustering. We show that this algorithm improves the quality of the overall clustering and manage to find the real structure and number of clusters of the global dataset.
This study is part of ADMIRE [15], a distributed data mining framework designed and developed at University College Dublin, Ireland.
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Aouad, L.M., Le-Khac, NA., Kechadi, T.M. (2007). Lightweight Clustering Technique for Distributed Data Mining Applications. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_10
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DOI: https://doi.org/10.1007/978-3-540-73435-2_10
Publisher Name: Springer, Berlin, Heidelberg
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