A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Findout: finding outliers in very large datasets
Knowledge and Information Systems
Algorithms for Spatial Outlier Detection
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Detecting and tracking regional outliers in meteorological data
Information Sciences: an International Journal
High performance computing for spatial outliers detection using parallel wavelet transform
Intelligent Data Analysis
Parallel wavelet transform for spatio-temporal outlier detection in large meteorological data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Fourier transform based spatial outlier mining
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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The increase use of high dimensional, geographically distributed rich and massive meteorological data poses an increasing scientific challenge in efficient outlier mining. Properties in such meteorological data are observed to fluctuate in spatial synchrony. Capturing this spatial variation at different spatial scales requires a multi-resolution analysis. In this paper, we develop an algorithm for region outlier detection at different scales using the multi-resolution feature of wavelet analysis. Another challenge of meteorological data mining is that the data size is huge to accommodate different resolutions and number of samples varies with the spatial scales. This motivated us to design a load adaptive parallel algorithm for outlier detection which can maintain good scalability for all spatial scales. Our algorithm has been implemented on high-performance computing architecture and evaluated on real-world meteorological data.