Method and apparatus for classifying time series data using...

Details Method and apparatus for classifying time series data using... Method and apparatus for classifying time series data using...

2005-03-22

2005-03-22

Nghiem, Michael (Department: 2863)

Data processing: measuring, calibrating, or testing

Measurement system

Measured signal processing

C382S232000, C702S182000

Reexamination Certificate

active

06871165

ABSTRACT:
A technique for effective classification of time series data using a rule-based wavelet decomposition approach. This method is effective in classification of a wide variety of time series data sets. The process uses a combination of wavelet decomposition, discretization and rule generation of training time series data to classify various instances of test time series data. The wavelet decomposition can effectively explore the data at varying levels of granularity to classify instances of the test time series data.

REFERENCES:
patent: 5848193 (1998-12-01), Garcia
patent: 6735550 (2004-05-01), Weekley et al.
J. Gehrke et al., “BOAT-Optimistic Decision Tree Construction,” SIGMOD Conference, 12 pages, 1999.*
J. Gehrke et al., “Rainforest—A Framework for Fast Decision Tree Construction of Large Datasets,” VLDB Conference, 12 pages, 1998.*
J. Gehrke et al., “Data Mining With Decision and Regression Trees,” ACM SIGKDD Conference Tutorial, slides KDD99 T1-1 through KDD99 T1-134, 70 pages, 1999.*
D.A. Keim et al., “Wavelets and their Applications in Databases,” ICDF Conference, pp. 1-60, 2001.*
J. Gehrke et al., “BOAT-Optimistic Decision Tree Construction,” SIGMOD Conference, 12 pages, 1999.
J. Gehrke et al., “Rainforest—A Framework for Fast Decision Tree Construction of Large Datasets,” VLDB Conference, 12 pages, 1998.
J. Gehrke et al., “Data Mining With Decision and Regression Trees,” ACM SIGKDD Conference Tutorial, slides KDD99 T1-1 through KDD99 T1-134, 70 pages, 1999.
D.A. Keim et al., “Wavelets and their Applications in Databases,” ICDE Conference, pp. 1-60, 2001.

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