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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barua, S., Alhajj, R.: High Performance Computing for Spatial Outliers Detection Using Parallel Wavelet Transform. Intelligent Data Analysis (in press)
Barua, S., Alhajj, R.: A Parallel Multi-scale Region Outlier Mining Algorithm for Meteorological Data. In: Proc. of ACM GIS (2007)
Birant, D., Kut, A.: Spatio-temporal outlier detection in large databases. Journal of Computing and Information Technology 14(4), 291–298 (2006)
Cheng, T., Li, Z.: A multiscale approach for spatio-temporal outlier detection. Transactions in GIS 10(2), 253–263 (2006)
Edwin, M.K., Raymond, T.N.: A unified notion of outliers: Properties and computation. In: Proc. of ACM-KDD, pp. 219–222 (1997)
Hung, E., Cheung, D.: Parallel algorithm for mining outliers in large database. In: Proc. of IDC (1999)
Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: Proc. of VLDB, pp. 392–403 (1998)
Ramachandran, R., Rushing, J., Conover, H., Graves, S., Keiser, K.: Flexible framework for mining meteorological data. In: Proc. of IIPS for Meteorology, Oceanography, and Hydrology (February 2003)
Ramaswamy, S., Alto, P., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proc. of ACM SIGMOD, pp. 427–438 (2000)
Shekhar, S., Chawla, S., Ravada, S., Fetterer, A., Liu, X., Lu, C.: Spatial databases-accomplishments and research needs. IEEE TKDE 11(1), 45–55 (1999)
Shekhar, S., Lu, C.-T., Zhang, P.: A unified approach to detecting spatial outliers. GeoInformatica 7(2) (2003)
Barnett, T.L.V.: Outliers in Statistical Data. John Wiley, New York (1994)
Yu, D., Sheikholeslami, G., Zhang, A.: Findout: finding outliers in very large datasets. Knowl. Inf. Syst. 4(4), 387–412 (2002)
Zhao, J., Lu, C., Kou, Y.: Detecting region outliers in meteorological data. In: Proc. of ACM International Symposium on Advances in GIS, pp. 49–55 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)