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HDSM: A distributed data mining approach to classifying vertically distributed data streams
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DOI：10.1016/j.knosys.2019.105114.png)
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Abstract
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The rise in the Internet of Things (IoT) and other sensor networks has created many vertically-distributed and high-velocity data streams that require specialized algorithms for true distributed data mining. This paper proposes a novel Hierarchical Distributed Stream Miner (HDSM) that learns relationships between the features of separate data streams with minimal data transmission to central locations. Experimental evaluation demonstrates significant improvements in classification accuracy over previously proposed distributed stream-mining approaches while minimizing data transmission and computational costs. HDSM's potential for dynamically trading off accuracy with computational resource costs is also demonstrated. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Distributed data stream mining
Vertically-distributed data
Online classification
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