Clustering
Clustering is an unsupervised learning technique used to group similar data points into sets called clusters. Unlike classification, clustering does not rely on pre-labeled data; instead, the algorithm identifies inherent structures in the dataset.
Popular methods
- K-Means clustering: groups data into k clusters based on distance to centroids.
- Hierarchical clustering: organizes data into nested clusters represented in a dendrogram.
- DBSCAN: groups dense regions and treats outliers as noise.
- Spectral clustering: leverages graph theory to partition complex datasets.
Applications
- Market segmentation to identify customer profiles.
- Document clustering for search engines or topic modeling.
- Healthcare: grouping patients by symptoms or treatment responses.
- Image segmentation in computer vision.
Clustering is often described as a way of “finding hidden patterns” in data. By grouping similar points together, it reveals structure that may not be obvious at first glance. What makes clustering powerful is its versatility: the same techniques can be applied to text, images, genetic data, or customer transactions.
Each method comes with trade-offs. K-Means is simple and efficient but struggles with irregularly shaped clusters. DBSCAN handles noise well but can be sensitive to parameter tuning. Spectral clustering excels at detecting non-convex shapes but is computationally expensive. Choosing the right algorithm depends on the dataset’s size, shape, and density.
In practice, clustering is not just about grouping points—it is often the first step in exploratory data analysis (EDA). Analysts use it to form hypotheses, reduce dimensionality, or generate features for supervised models. However, evaluation can be tricky since there are no ground-truth labels. Metrics like the silhouette score, the Davies-Bouldin index, or human interpretability help measure clustering quality.
Reference
- Aggarwal, C. C., & Reddy, C. K. (2013). Data Clustering: Algorithms and Applications. Chapman & Hall/CRC.
