# Outlier Analysis

Data mining typically looks for general patterns, but can also identify abnormal patterns or anomalous data points.

Anomalous data points can be errors, but are potentially significant events.

# Types

• Global (differs from entire data set)
• Contextual (differs from local context)
• Collective (group of points differ from local or global context)

# Challenges in detection

• Normal vs. abnormal (no clear definition, varies by application, noisy data)
• Efficiency (latency, scalability, adaptability)
• Interpretability (how to explain the results)

# Detection Methods

• Ground truth label (vetted cases of anomalous activity)

In real world setting you may use semi-supervised learning.

• Assumption of normal vs. abnormal (domain knowledge)

Can also generate "domain knowledge" using formal statistical models.

Identify "normal" with clustering and consider abnormal points using distance and density measurements.

# Classification

# Supervised

You need a "large" sample of vetted normal/abnormal classes. You likely will have more than one class that is considered abnormal and potentially more than one class that is considered normal.

Challenges:

• Class imbalance
• New patterns (ex: new type of security attack)

# Unsupervised Clustering

Leverages the assumption of majority vs minority clusters to identify what is anomalous. Typically have multiple clusters for normal and multiple clusters for abnormal.

This approach is fairly general to different applications.

• Distance based (absolute proximity like kNN)
• Density based (relative proximity)

# Semi supervised Methods

Partial labels of normal and abnormal cases, use a combination of clustering and classification.

• normal: large clusters and/or normal cases
• abnormal: small clusters and/or abnormal cases

# Contextual Anomaly

Context features to domain impact behavior of data

• not an abnormal data point if low wind day has low wind power output

You can identify context with frequent pattern analysis, subspace approaches, or domain-specific approaches.

Once you have context identified, you can transition back to thinking about it as a global anomaly detection problem.

# Collective Anomaly

A group of objects deviate from the norm.

Identify structural relationships among objects.

Group related objects into a "super object", and transition back to global outlier detection.