What is Anomaly Detection?
Anomalies, also known as outliers, are data points that deviate significantly from the expected behavior or norm within a dataset. They are crucial to identify because they can signal potential problems, fraudulent activities, or interesting discoveries. Anomaly detection plays a vital role in various fields, including data analysis, machine learning, and network security.
Types of Anomalies
There are essentially three types of anomalies: point anomalies, contextual anomalies, and collective anomalies.
- Point Anomalies (Global Anomalies): These are the most basic type, representing individual data points that are statistically unusual compared to the rest of the data. For instance, a credit card transaction with an exceptionally high amount might be flagged as a point anomaly.
- Contextual Anomalies (Conditional Anomalies): These anomalies depend on the specific context or environment surrounding them. They often occur in time-series data, where patterns can change over time. An example is a sudden spike in temperature during winter within weather data.
- Collective Anomalies: These involve groups of related data points exhibiting abnormal behavior collectively, even if individually they might seem normal. They disrupt the overall data distribution. Identifying collective anomalies often requires complex pattern-based algorithms, and they are commonly found in dynamic environments like network traffic data.
Anomaly detection using Isolation Forest
Anomaly detection is vital across industries, revealing outliers in data that signal problems or unique insights. Isolation Forests offer a powerful solution, isolating anomalies from normal data. In this tutorial, we will explore the Isolation Forest algorithm’s implementation for anomaly detection using the Iris flower dataset, showcasing its effectiveness in identifying outliers amidst multidimensional data.