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• W2598498736 abstract "Data stream clustering with sliding windows generates clusters for every window movement. Because repeated clustering on all changed windows is highly inefficient in terms of memory and computation time, a clustering algorithm should be designed with considering only inserted and deleted tuples of windows. In this paper, we address this problem by sliding window aggregation technique and cluster modification strategy. We propose a novel data structure for construction and maintenance of 2-level synopses. This data structure enables to update synopses efficiently and support precise sliding window operations. We also suggest a modification strategy to decide whether to append new synopses to pre-existing clusters or perform clustering on whole synopses according to the difference between probability distributions of the original and updated clusters. Experimental results show that proposed method outperforms state-of-the-art methods." @default.
• W2598498736 created "2017-04-07" @default.
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• W2598498736 creator A5077087855 @default.
• W2598498736 date "2017-01-01" @default.
• W2598498736 modified "2023-09-24" @default.
• W2598498736 title "Partition-Based Clustering with Sliding Windows for Data Streams" @default.
• W2598498736 cites W1522565396 @default.
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• W2598498736 doi "https://doi.org/10.1007/978-3-319-55699-4_18" @default.
• W2598498736 hasPublicationYear "2017" @default.
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