Abstract
Uncertainty is inherent in data streams, and presents new challenges to data streams mining. For continuous arriving and large size of data streams, representations of uncertain time series data streams require significantly more space. Therefore, it is important to construct compressed representation for storing uncertain time series data. A granular sketch is designed to create hash-compressed storage and store granules. As the granular sketch may be saturated with the increasing of data streams, this paper presents an optimization strategy to delete the absolute sparse patterns. Based on the granular sketch, a sequential pattern mining algorithm is proposed for mining uncertain data streams. The experimental results illustrate the effectiveness of the pattern mining algorithm.
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Chen, J., Chen, P., Sheng, X. (2013). Granular Sketch Based Uncertain Data Streams Pattern Mining. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53932-9_48
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DOI: https://doi.org/10.1007/978-3-642-53932-9_48
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