Clustering in Machine Learning: What It Is and How It Works
Clustering in machine learning (ML) groups similar data points into clusters to identify patterns without prior labels. It differs from classification, which requires labeled data. Key steps include measuring data similarity, grouping points into clusters, deciding the number of clusters, and choosing between hard and soft clustering methods. Common algorithms include k-means (centroid-based), hierarchical, density-based (e.g., DBSCAN), and distribution-based (e.g., Gaussian mixture models). Applications range from recommendations to anomaly detection. Advantages include scalability and aiding data exploration, while challenges involve interpretability and sensitivity to parameters in high-dimensional data.
