data engineering topic on cluster analysis
- 1. 1 Data Mining: Concepts and Techniques (3rd ed.) — Chapter 12 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved.
- 2. 2 Chapter 12. Outlier Analysis Outlier and Outlier Analysis Outlier Detection Methods
- 3. 3 What Are Outliers? Outlier: A data object that deviates significantly from the normal objects as if it were generated by a different mechanism Ex.: Unusual credit card purchase, sports: Michael Jordon, Wayne Gretzky, ... Outliers are different from the noise data Noise is random error or variance in a measured variable Noise should be removed before outlier detection Outliers are interesting: It violates the mechanism that generates the normal data Outlier detection vs. novelty detection: early stage, outlier; but later merged into the model Applications: Credit card fraud detection Telecom fraud detection Customer segmentation Medical analysis
- 4. 4 Types of Outliers (I) Three kinds: global, contextual and collective outliers Global outlier (or point anomaly) Object is Og if it significantly deviates from the rest of the data set Ex. Intrusion detection in computer networks Issue: Find an appropriate measurement of deviation Contextual outlier (or conditional outlier) Object is Oc if it deviates significantly based on a selected context Ex. 80o F in Urbana: outlier? (depending on summer or winter?) Attributes of data objects should be divided into two groups Contextual attributes: defines the context, e.g., time & location Behavioral attributes: characteristics of the object, used in outlier evaluation, e.g., temperature Can be viewed as a generalization of local outliers—whose density significantly deviates from its local area Issue: How to define or formulate meaningful context? Global Outlier
- 5. 5 Types of Outliers (II) Collective Outliers A subset of data objects collectively deviate significantly from the whole data set, even if the individual data objects may not be outliers Applications: E.g., intrusion detection: When a number of computers keep sending denial-of-service packages to each other Collective Outlier Detection of collective outliers Consider not only behavior of individual objects, but also that of groups of objects Need to have the background knowledge on the relationship among data objects, such as a distance or similarity measure on objects. A data set may have multiple types of outlier One object may belong to more than one type of outlier
- 6. 6 Challenges of Outlier Detection Modeling normal objects and outliers properly Hard to enumerate all possible normal behaviors in an application The border between normal and outlier objects is often a gray area Application-specific outlier detection Choice of distance measure among objects and the model of relationship among objects are often application-dependent E.g., clinic data: a small deviation could be an outlier; while in marketing analysis, larger fluctuations Handling noise in outlier detection Noise may distort the normal objects and blur the distinction between normal objects and outliers. It may help hide outliers and reduce the effectiveness of outlier detection Understandability Understand why these are outliers: Justification of the detection Specify the degree of an outlier: the unlikelihood of the object being generated by a normal mechanism
- 7. 7 Chapter 12. Outlier Analysis Outlier and Outlier Analysis Outlier Detection Methods
- 8. Outlier Detection I: Supervised Methods Two ways to categorize outlier detection methods: Based on whether user-labeled examples of outliers can be obtained: Supervised, semi-supervised vs. unsupervised methods Based on assumptions about normal data and outliers: Statistical, proximity-based, and clustering-based methods Outlier Detection I: Supervised Methods Modeling outlier detection as a classification problem Samples examined by domain experts used for training & testing Methods for Learning a classifier for outlier detection effectively: Model normal objects & report those not matching the model as outliers, or Model outliers and treat those not matching the model as normal Challenges Imbalanced classes, i.e., outliers are rare: Boost the outlier class and make up some artificial outliers Catch as many outliers as possible, i.e., recall is more important than accuracy (i.e., not mislabeling normal objects as outliers) 8
- 9. Outlier Detection II: Unsupervised Methods Assume the normal objects are somewhat ``clustered'‘ into multiple groups, each having some distinct features An outlier is expected to be far away from any groups of normal objects Weakness: Cannot detect collective outlier effectively Normal objects may not share any strong patterns, but the collective outliers may share high similarity in a small area Ex. In some intrusion or virus detection, normal activities are diverse Unsupervised methods may have a high false positive rate but still miss many real outliers. Supervised methods can be more effective, e.g., identify attacking some key resources Many clustering methods can be adapted for unsupervised methods Find clusters, then outliers: not belonging to any cluster Problem 1: Hard to distinguish noise from outliers Problem 2: Costly since first clustering: but far less outliers than normal objects Newer methods: tackle outliers directly 9
- 10. Outlier Detection III: Semi-Supervised Methods Situation: In many applications, the number of labeled data is often small: Labels could be on outliers only, normal objects only, or both Semi-supervised outlier detection: Regarded as applications of semi- supervised learning If some labeled normal objects are available Use the labeled examples and the proximate unlabeled objects to train a model for normal objects Those not fitting the model of normal objects are detected as outliers If only some labeled outliers are available, a small number of labeled outliers many not cover the possible outliers well To improve the quality of outlier detection, one can get help from models for normal objects learned from unsupervised methods 10
- 11. Outlier Detection (1): Statistical Methods Statistical methods (also known as model-based methods) assume that the normal data follow some statistical model (a stochastic model) The data not following the model are outliers. 11 Effectiveness of statistical methods: highly depends on whether the assumption of statistical model holds in the real data There are rich alternatives to use various statistical models E.g., parametric vs. non-parametric Example (right figure): First use Gaussian distribution to model the normal data For each object y in region R, estimate gD(y), the probability of y fits the Gaussian distribution If gD(y) is very low, y is unlikely generated by the Gaussian model, thus an outlier
- 12. Outlier Detection (2): Proximity-Based Methods An object is an outlier if the nearest neighbors of the object are far away, i.e., the proximity of the object is significantly deviates from the proximity of most of the other objects in the same data set 12 The effectiveness of proximity-based methods highly relies on the proximity measure. In some applications, proximity or distance measures cannot be obtained easily. Often have a difficulty in finding a group of outliers which stay close to each other Two major types of proximity-based outlier detection Distance-based vs. density-based Example (right figure): Model the proximity of an object using its 3 nearest neighbors Objects in region R are substantially different from other objects in the data set. Thus the objects in R are outliers
- 13. Outlier Detection (3): Clustering-Based Methods Normal data belong to large and dense clusters, whereas outliers belong to small or sparse clusters, or do not belong to any clusters 13 Since there are many clustering methods, there are many clustering-based outlier detection methods as well Clustering is expensive: straightforward adaption of a clustering method for outlier detection can be costly and does not scale up well for large data sets Example (right figure): two clusters All points not in R form a large cluster The two points in R form a tiny cluster, thus are outliers