clustering

Clustering is a task in unsupervised learning and statistics that groups objects so that those within a cluster are more similar to each other than to objects in other clusters. The goal is to discover structure, patterns, or subgroups without predefined labels.

Clustering relies on similarity or distance and supports numerical, categorical, or mixed data. Algorithms fall into partitioning, hierarchical, density-based, grid-based, and model-based families, with examples such as k-means, hierarchical clustering, DBSCAN, and Gaussian mixture models.

Partitioning methods assign objects to a predefined number of clusters by optimizing cohesion. Hierarchical clustering builds a cluster tree through merging or splitting using various linkage criteria, while density-based methods identify dense regions separated by sparse areas.

Model-based clustering assumes data arise from a mixture of distributions; parameters are inferred by methods such as Expectation-Maximization. Grid-based methods quantize the feature space into cells and cluster adjacent dense cells.

Distance measures and scaling influence results. Common metrics include Euclidean, Manhattan, cosine, and Jaccard for binary or categorical data.

Evaluation uses internal indices, such as the silhouette coefficient and Davies-Bouldin index, or external indices like the Rand index and Mutual Information when a ground truth is available.

Applications include market segmentation, image and text clustering, bioinformatics, anomaly detection, and social network analysis.

Limitations include choosing the number of clusters, sensitivity to outliers and scaling, difficulties in high-dimensional data, and challenges in interpreting and validating clusters. The field dates to mid-20th century developments, with k-means popularized in the 1950s–1980s.