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Scalable Cluster Tendency Assessment for Streaming Activity Data using Recurring Shapelets
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posted on 2023-02-14, 04:15 authored by S Datta, Chandan KarmakarChandan Karmakar, P Rathore, M PalaniswamiAutomatic interpretation of cluster structure in rapidly arriving data streams is essential for timely detection of interesting events. Human activities often contain bursts of repeating patterns. In this paper, we propose a new relative of the Visual Assessment of Cluster Tendency (VAT) model, to interpret cluster evolution in streaming activity data where shapes of recurring patterns are important. Existing VAT algorithms are either suitable only for small batch data and unscalable to rapidly evolving streams, or cannot capture shape patterns. Our proposed incremental algorithm processes streaming data in chunks and identifies repeating patterns or shapelets from each chunk, creating a Dictionary-of-Shapes (DoS) that is updated on the fly. Each chunk is transformed into a lower dimensional representation based on it's distance from the shapelets in the current DoS. Then a small set of transformed chunks are sampled using an intelligent Maximin Random Sampling (MMRS) scheme, to create a scalable VAT image that is incrementally updated as the data stream progresses. Experiments on two upper limb activity datasets demonstrate that the proposed method can successfully and efficiently visualize clusters in long streams of data and can also identify anomalous movements.
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Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSVolume
2022-JulyPagination
1036-1040Location
Piscataway, N.J.Publisher DOI
ISSN
1557-170XeISSN
2694-0604Language
engPublication classification
C1.1 Refereed article in a scholarly journalPublisher
IEEEUsage metrics
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