Abstract
Distributed data streams mining is increasingly demanded in most extensive application domains, like web traffic analysis and financial transactions. In distributed environments, it is impractical to transmit all data to one node for global model. It is reasonable to extract the essential parts of local models of subsidiary nodes, thereby integrating into the global model. In this paper we proposed an approach SVDDS to do this model integration in distributed environments. It is based on SVM theory, and trades off between the risk of the global model and the total transmission load. Our analysis and experiments show that SVDDS obviously lowers the total transmission load while the global accuracy drops comparatively little.
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Wang, P., Mao, G. (2010). Describing Data with the Support Vector Shell in Distributed Environments. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_10
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DOI: https://doi.org/10.1007/978-3-642-14400-4_10
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