Abstract
The trend cluster discovery retrieves areas of spatially close sensors which measure a numeric random field having a prominent data trend along a time horizon. We propose a computation preserving algorithm which employees an incremental learning strategy to continuously maintain sliding window trend clusters across a sensor network. Our proposal reduces the amount of data to be processed and saves the computation time as a consequence. An empirical study proves the effectiveness of the proposed algorithm to take under control computation cost of detecting sliding window trend clusters.
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Appice, A., Malerba, D., Ciampi, A. (2012). Continuously Mining Sliding Window Trend Clusters in a Sensor Network. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32597-7_22
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DOI: https://doi.org/10.1007/978-3-642-32597-7_22
Publisher Name: Springer, Berlin, Heidelberg
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