What Are Outliers/Anomalies ?

Outliers: A data object that deviates significantly from the normal objects as if it were generated by a different mechanism.

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 (identify new topics and trends in a timely manner in social media): early stage, outlier; but later merged into the model
• There applications are :
  • Credit card fraud detection
  • Telecom fraud detection
  • Customer segmentation
  • Medical analysis

Importance of Anomaly Detection

Ozone Depletion History

• In 1977 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 researchers held back publishing their work for nearly a decade.
• The ozone concentrations recorded by the satellite were so low they were being treated as outliers by a computer program and discarded!

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

Types of Outliers

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?
• 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
• 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

 

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)

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

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

Mining Contextual Outliers:
Transform into Conventional Outlier Detection

If the contexts can be clearly identified, transform it to conventional outlier detection

1. Identify the context of the object using the contextual attributes
2. Calculate the outlier score for the object in the context using a conventional outlier detection method

• Ex. Detect outlier customers in the context of customer groups
• Contextual attributes: age group, postal code
• Behavioral attributes: # of trans/yr, annual total trans. amount

Steps:
(1) locate c’s context,
(2) compare c with the other customers in the same group, and
(3) use a conventional outlier detection method



 

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