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Chap10 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
Anomaly/Outlier Detection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Importance of Anomaly Detection ,[object Object],[object Object],[object Object],[object Object],Sources: http://exploringdata.cqu.edu.au/ozone.html http://www.epa.gov/ozone/science/hole/size.html
Anomaly Detection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Anomaly Detection Schemes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Graphical Approaches ,[object Object],[object Object],[object Object],[object Object]
Convex Hull Method ,[object Object],[object Object],[object Object]
Statistical Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object]
Grubbs’ Test ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Statistical-based – Likelihood Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Statistical-based – Likelihood Approach ,[object Object],[object Object],[object Object],[object Object],[object Object]
Limitations of Statistical Approaches ,[object Object],[object Object],[object Object]
Distance-based Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object]
Nearest-Neighbor Based Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outliers in Lower Dimensional Projection ,[object Object],[object Object],[object Object],[object Object]
Outliers in Lower Dimensional Projection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example ,[object Object]
Density-based: LOF approach ,[object Object],[object Object],[object Object],In the NN approach, p 2 is not considered as outlier, while LOF approach find both p 1 and p 2 as outliers p 2  p 1 
Clustering-Based ,[object Object],[object Object],[object Object],[object Object],[object Object]
Base Rate Fallacy ,[object Object],[object Object]
Base Rate Fallacy (Axelsson, 1999)
Base Rate Fallacy ,[object Object]
Base Rate Fallacy in Intrusion Detection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Detection Rate vs False Alarm Rate ,[object Object],[object Object],[object Object]
Detection Rate vs False Alarm Rate ,[object Object]

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Chap10 Anomaly Detection

  • 1. 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
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  • 21. Base Rate Fallacy (Axelsson, 1999)
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