Unsupervised learning finds patterns in data without labeled outcomes. Unlike supervised learning, there are no predefined answers—algorithms discover hidden structures on their own. This section covers clustering algorithms that group similar data points, association rules that find relationships between variables, and anomaly detection for identifying unusual observations
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.
Association and anomaly detection are key unsupervised learning techniques used to uncover hidden patterns and identify unusual behaviors in data. Association methods find meaningful relationships between items, while anomaly detection spots rare or abnormal patterns. These techniques help improve recommendations, detect fraud, and enhance system monitoring across various industries.
Lessons are available in the subtopics above.
