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chap10_anomaly_detection - Data Mining Anomaly Detection...

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Data Mining Anomaly Detection Lecture Notes for Chapter 10 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Anomaly/Outlier Detection What are anomalies/outliers? The set of data points that are considerably different than the remainder of the data Variants of Anomaly/Outlier Detection Problems Given a database D, find all the data points x D with anomaly scores greater than some threshold t Given a database D, find all the data points x D having the top- n largest anomaly scores f( x ) Given a database D, containing mostly normal (but unlabeled) data points, and a test point x , compute the anomaly score of x with respect to D Applications: Credit card fraud detection, telecommunication fraud detection, network intrusion detection, fault detection
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Importance of Anomaly Detection Ozone Depletion History In 1985 three researchers (Farman, Gardinar and Shanklin) were puzzled by data gathered by the British Antarctic Survey showing that ozone levels for Antarctica had dropped 10% below normal levels Why did the Nimbus 7 satellite, which had instruments aboard for recording ozone levels, not record similarly low ozone concentrations? The ozone concentrations recorded by the satellite were so low they were being treated as outliers by a computer program and discarded! Sources: http://exploringdata.cqu.edu.au/ozone.html http://www.epa.gov/ozone/science/hole/size.html
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Anomaly Detection Challenges How many outliers are there in the data? Method is unsupervised Validation can be quite challenging (just like for clustering) Finding needle in a haystack Working assumption: There are considerably more “normal” observations than “abnormal” observations (outliers/anomalies) in the data
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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Anomaly Detection Schemes General Steps Build a profile of the “normal” behavior
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