Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Spatial Databases-Accomplishments and Research Needs
IEEE Transactions on Knowledge and Data Engineering
Findout: finding outliers in very large datasets
Knowledge and Information Systems
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
Detecting region outliers in meteorological data
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
A parallel multi-scale region outlier mining algorithm for meteorological data
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
High performance computing for spatial outliers detection using parallel wavelet transform
Intelligent Data Analysis
Parallel k-most similar neighbor classifier for mixed data
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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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.