Data processing: artificial intelligence – Machine learning
Reexamination Certificate
2011-08-23
2011-08-23
Holmes, Michael B (Department: 2129)
Data processing: artificial intelligence
Machine learning
Reexamination Certificate
active
08005771
ABSTRACT:
A method and framework are described for detecting changes in a multivariate data stream. A training set is formed by sampling time windows in a data stream containing data reflecting normal conditions. A histogram is created to summarize each window of data, and data within the histograms are clustered to form test distribution representatives to minimize the bulk of training data. Test data is then summarized using histograms representing time windows of data and data within the test histograms are clustered. The test histograms are compared to the training histograms using nearest neighbor techniques on the clustered data. Distances from the test histograms to the test distribution representatives are compared to a threshold to identify anomalies.
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Chen Terrence
Neubauer Claus
Sheikh Abdul Saboor
Yuan Chao
Holmes Michael B
Siemens Corporation
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