Abstract
In this paper, we investigate the problem of clustering distributed multidimensional data streams. We devise a distributed clustering framework DistClusTree that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication cost and clustering quality. We tackle this in DistClusTree through combining spatial index summaries and online tracking for efficient local and global incremental clustering. We demonstrate through extensive experiments the efficacy of the framework in terms of communication cost and approximate clustering quality.
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Razavi Hesabi, Z., Sellis, T., Liao, K. (2018). DistClusTree: A Framework for Distributed Stream Clustering. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_23
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DOI: https://doi.org/10.1007/978-3-319-92013-9_23
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