Clustering

Clustering, in the context of anomaly detection, is a technique used to group data points into clusters or groups based on their similarity or proximity. The primary goal of clustering in anomaly detection is to identify patterns or structures within the data so that anomalies, which deviate significantly from these patterns, can be detected more effectively.

How Clustering is Applied in Anomaly Detection

1. Data Representation: Before applying clustering, the data is usually transformed or represented in a suitable format. For instance, numerical features may need to be standardized or normalized, and categorical features may be one-hot encoded or otherwise prepared;
2. Clustering Algorithm: A clustering algorithm, such as K-Means, DBSCAN, hierarchical clustering, or Gaussian Mixture Models (GMM), is applied to the prepared data. These algorithms group similar data points together based on distance metrics or probabilistic models;
3. Cluster Formation: The algorithm partitions the data into clusters. Each cluster contains data points that are similar or closely related to each other in some way, such as in terms of distance or density;
4. Anomaly Detection: Anomalies or outliers are detected by assessing how well data points fit within their respective clusters. Data points that are significantly different from their cluster's characteristics are considered anomalies.

Advantages and Limitations

Clustering-based anomaly detection has its advantages and limitations. It is particularly useful when anomalies have distinct patterns or are isolated from the majority of the data. However, it may not perform well when anomalies are mixed with normal data points within clusters or when the number of clusters is not well-defined.

In practice, combining clustering with other anomaly detection techniques, such as statistical methods or machine learning algorithms, can provide more robust and accurate results. This hybrid approach leverages the strengths of different methods to improve anomaly detection performance in various real-world scenarios.

Implementation example

In the provided code sample for anomaly detection using K-Means clustering, the threshold is calculated as the 95th percentile of distances between data points and their assigned cluster centers. This means that 95% of the distances should be below this threshold, and any data points with distances exceeding it are considered anomalies.