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. © 2012 Springer-Verlag.
CITATION STYLE
Appice, A., Malerba, D., & Ciampi, A. (2012). Continuously mining sliding window trend clusters in a sensor network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7447 LNCS, pp. 248–255). https://doi.org/10.1007/978-3-642-32597-7_22
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