Data processing: artificial intelligence – Knowledge processing system
Reexamination Certificate
2011-01-04
2011-01-04
Starks, Jr., Wilbert L (Department: 2129)
Data processing: artificial intelligence
Knowledge processing system
Reexamination Certificate
active
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.
Y. Zhang, et al., A Taxonomy Framework for Unsupervised Outlier Detection Techniques for Multi-Type Data Sets. Technical Report TR-CTIT-07-79, Enschede, Nov. 2007.
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.
E.M. Knorr et al., “Algorithms For Mining Distance-Based Outliers in Large Datasets,” Proceedings of the 24th VLDB Conference, New York, USA, pp. 392-403, 1998.
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.
Aggarwal Charu C.
Yu Philip Shi-Lung
Okamoto & Benedicto LLP
Starks, Jr. Wilbert L
Trend Micro Incorporated
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