Data Mining Anomaly/Outlier Detection Lecture Notes for Chapter 10 Introduction to Data Mining by Tan, Steinbach, Kumar

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Anomaly/Outlier Detection l What are anomalies/outliers? –The set of data points that are considerably different than the remainder of the data l 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 l Applications: –Credit card fraud detection, telecommunication fraud detection, network intrusion detection, fault detection

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Importance of Anomaly Detection Ozone Depletion History l 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 l Why did the Nimbus 7 satellite, which had instruments aboard for recording ozone levels, not record similarly low ozone concentrations? l The ozone concentrations recorded by the satellite were so low they were being treated as outliers by a computer program and discarded! Sources:

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Anomaly Detection l 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 l Working assumption: –There are considerably more “normal” observations than “abnormal” observations (outliers/anomalies) in the data

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Anomaly Detection Schemes l General Steps –Build a profile of the “normal” behavior  Profile can be patterns or summary statistics for the overall population –Use the “normal” profile to detect anomalies  Anomalies are observations whose characteristics differ significantly from the normal profile l Types of anomaly detection schemes –Graphical –Model-based –Distance-based –Clustering-based

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Graphical Approaches l Boxplot (1-D), Scatter plot (2-D), Spin plot (3-D) l Limitations –Time consuming –Subjective e.g.: data are outliers if the more than 1.5 IQR (or 3 IQR) away from the box!

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ R Box Plots (different from textbook) l R Box Plots –Invented by J. Tukey –Another way of displaying the distribution of data –Following figure shows the basic part of a box plot outlier Minimum (or at most 1.5 IQR off the 25 th percentile) 25 th percentile 75 th percentile 50 th percentile Maximum (or at most 1.5 IQR off the 75 th percentile) IQR Corrected on: 9/9/2013

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Convex Hull Method l Extreme points are assumed to be outliers l Use convex hull method to detect extreme values l l Approach: Fit a polygon to a point set; outliers are determined by their distance to the polygon boundary

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Statistical Approaches---Model-based l Fit a parametric model M describing the distribution of the data (e.g., normal distribution) l Assess the probability of each point M l The lower a point’s probability the more likely the point is an outlier. l Outlier Detection Approach: –Sort points by the probability –Determine Outliers  based on a probability threshold  take the bottom x percent as outliers.

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Limitations of Statistical Approaches l Many methods have been designed for a single attribute and are not easy to generalize to multi- dimensional data l In many cases, data distribution/model may not be known, and might not match the assumoptions of the employed density function (e.g. not symmetric, not Gaussian,…) l For high dimensional data, it may be difficult to estimate the true distribution, but there is new work based on EM and mixtures of Gaussians

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Distance-based Approaches l Data is represented as a vector of features l Three major approaches –Nearest-neighbor based –Density based –Clustering based

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Nearest-Neighbor Based Approach l Approach: –Compute the distance between every pair of data points –There are various ways to define outliers:  Data points for which there are fewer than p neighboring points within a distance D  The top n data points whose distance to the kth nearest neighbor is greatest  The top n data points whose average distance to the k nearest neighbors is greatest

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Density-based: LOF approach l For each point, compute the density of its local neighborhood; e.g. use DBSCAN’s approach l Compute local outlier factor (LOF) of a sample p as the average of the ratios of the density of sample p and the density of its nearest neighbors l Outliers are points with lowest LOF value p 2  p 1  In the NN approach, p 2 is not considered as outlier, while LOF approach find both p 1 and p 2 as outliers Alternative approach: directly use density function; e.g. DENCLUE’s density function

© Tan,Steinbach, Kumar Outlier Detection (edited by Ch. Eick) 4/8/ Clustering-Based l Idea: Use a clustering algorithm that has some notion of outliers! l Problem what parameters should I choose for the algorithm; e.g. DBSCAN? l Rule of Thumb: Less than x% of the data should be outliers (with x typically chosen between 0.1 and 10); x might be determined with other methods; e.g. statistical tests or domain knowledge. l Once you have an idea, how much outliers you want, the parameter selection problem becomes easier.