Abstract
Streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding window model for processing and mining data streams. However, for some applications, other stream processing models such as the time-fading model are more appropriate. Moreover, batches of data in the stream may be delayed and not arrived in the intended order. In this paper, we propose mining algorithms that use the time-fading model to mine frequent patterns when these batches in the streams of uncertain data were delayed and arrived out of order.
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Jiang, F., Leung, C.KS. (2013). Stream Mining of Frequent Patterns from Delayed Batches of Uncertain Data. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_18
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DOI: https://doi.org/10.1007/978-3-642-40131-2_18
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