Abstract
This paper describes a state-of-the-art parallel data mining solution that employs wavelet analysis for scalable outlier detection in large complex spatio-temporal data. The algorithm has been implemented on multiprocessor architecture and evaluated on real-world meteorological data. Our solution on high-performance architecture can process massive and complex spatial data at reasonable time and yields improved prediction.
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Barua, S., Alhajj, R. (2007). Parallel Wavelet Transform for Spatio-temporal Outlier Detection in Large Meteorological Data. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_69
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DOI: https://doi.org/10.1007/978-3-540-77226-2_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
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