Apparatus for dynamic classification of data in evolving...

Data processing: database and file management or data structures – Database design – Data structure types

Reexamination Certificate

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C707S793000, C707S793000

Reexamination Certificate

active

07487167

ABSTRACT:
A technique for classifying data from a test data stream is provided. A stream of training data having class labels is received. One or more class-specific clusters of the training data are determined and stored. At least one test instance of the test data stream is classified using the one or more class-specific clusters.

REFERENCES:
Hulten et al., “Mining Time-Changing Data Streams”, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
B. Babcock et al., “Models and Issues in Data Stream Systems,”ACM PODS Conference,pp. 1-30, 2002.
P. Domingos et al., “Mining High-Speed Data Streams,” ACM SIGKDD Conference, 10 pages, 2000.
J. Feigenbaum et al., “Testing and Spot-Checking of Data Streams,” ACM SODA Conference, pp. 1-14, 2000.
J. Fong et al., “An ApproximateLP-Difference Algorithm for Massive Data Streams,” Annual Symposium on Theoretical Aspects in Computer Science (STACS), pp. 193-204, 2000.
J. Gehrke et al., “On Computing Correlated Aggregates Over Continual Data Streams,” ACM SIGMOD Conference, 12 pages, 2001.
S. Guha et al., “Clustering Data Streams,”IEEE FOCS Conference, pp. 1-8, 2000.
L. O'Callaghan et al., “Streaming-Data Algorithms for High Quality Clustering,” ICDE Conference, pp. 1-25, 2002.
B.-K. Yi et al., “Online Data Mining for Co-Evolving Time Sequences,” ICDE Conference, pp. 1-26, 2000.
J.H. Friedman, “Recursive Partitioning Decision Rule for Non-Parametric Classifiers,” IEEE Transactions on Computer, C-26, pp. 404-408, 1977.
M. Garofalakis et al., “Efficient Algorithms for Constructing Decision Trees with Constraints,” KDD Conference, pp. 335-339, 2000.
J. Gehrke et al., “BOAT-Optimistic Decision Tree Construction,” ACM SIGMOD Conference Proceedings, pp. 169-180, 1999.
J. Gehrke et al., “Rainforest-A Framework for Fast Decision Tree Construction of Large Datasets,” VLDB Conference Proceedings, pp. 127-162, 1998.
G. Hulten et al., “Mining Time-Changing Data Streams,” ACM KDD Conference, 10 pages, 2001.
T. Zhang et al., “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” ACM SIGMOD Conference, pp. 103-114, Canada, 1996.

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