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SEGMENT-BASED CHANGE DETECTION METHOD IN MULTIVARIATE DATA STREAM
SEGMENT-BASED CHANGE DETECTION METHOD IN MULTIVARIATE DATA STREAM
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机译:多元数据流中基于段的变化检测方法
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摘要
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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