Clustering algorithms are unsupervised learning methods used to group similar data points without predefined labels. They reveal hidden patterns, segment datasets, and help identify natural groupings in data. Popular techniques like K-Means, Hierarchical Clustering, and DBSCAN support applications in customer segmentation, pattern recognition, and anomaly detection.
Learn how K-Means Clustering groups similar data points into meaningful clusters. This guide covers the algorithm’s workflow, distance metrics, choosing optimal K, and practical applications in customer segmentation and pattern discovery.
Understand Hierarchical Clustering, a method that builds clusters step‑by‑step to reveal data structure. This lesson explains dendrograms, linkage methods, and how to identify natural groupings without predefining the number of clusters.
Explore DBSCAN, a powerful clustering algorithm that identifies dense regions and detects noise. Learn how it discovers clusters of any shape and performs well with outliers and complex datasets.
