Methods and apparatus for outlier detection for high...

Data processing: artificial intelligence – Knowledge processing system

Reexamination Certificate

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Reexamination Certificate

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07865456

ABSTRACT:
Methods and apparatus are provided for outlier detection in databases by determining sparse low dimensional projections. These sparse projections are used for the purpose of determining which points are outliers. The methodologies of the invention are very relevant in providing a novel definition of exceptions or outliers for the high dimensional domain of data.

REFERENCES:
patent: 7395250 (2008-07-01), Aggarwal et al.
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C.C. Aggarwal et al., “Finding Generalized Projected Clusters in High Dimensional Spaces,” Proceedings of the ACM SIGMOD Conference, pp. 1-12, 2000.
M.M. Breunig et al., “LOF: Identifying Density-Based Local Outliers,” Proc. ACM SIGMOD 2000 Int. Conf. on Mangement of Data, Dallas, TX, pp. 1-12, 2000.
S. Ramaswamy et al., “Efficient Algorithms For Mining Outliers From Large Data Sets,” Proceedings of the ACM SIGMOD Conference, pp. 1-20, 2000.
C.C. Aggarwal et al., “Fast Algorithms for Projected Clustering,” Proceedings of the ACM SIGMOD Conference, pp. 1-12. 1999.
K. Beyer et al., “When Is ‘Nearest Neighbor’ Meaningful?,” Proceedings of the ICDT, pp. 1-19, 1999.
E.M. Knorr et al., “Finding Intensional Knowledge of Distance-Based Outliers,” Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, pp. 211-222, 1999.
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A. Arning et al., “A Linear Method for Deviation Detection in Large Databases,” Proceedings of the KDD Conference, pp. 1-6, 1995.
S. Sarawagi et al., “Discovery-Driven Exploration of OLAP Data Cubes,” IBM Almaden Research Center, San José, CA, pp. 1-15.

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