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.

Continuously Mining Sliding Window Trend Clusters in a Sensor Network

APPICE, ANNALISA;MALERBA, Donato;
2012

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.
978-3-642-32596-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/111769
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