Unsupervised Learning

Unsupervised learning is a machine learning technique that automatically discovers structure and patterns in unlabeled data. Without any "correct answers" provided, it groups data, reduces dimensions, and performs other operations based on the distribution and relationships within the data. Whereas supervised learning is used for classification and prediction with labeled data, unsupervised learning is widely used for exploratory data analysis, feature extraction, and preprocessing — wherever the underlying structure is unknown. Major applications: • Clustering (grouping data) └ Examples: Automatic customer segmentation, marketing analysis, anomaly detection • Dimensionality reduction (feature compression) └ Examples: Visualization and acceleration using PCA or t-SNE • Feature extraction and representation learning └ Examples: Learning latent features with autoencoders • Anomaly detection └ Identifying deviations from normal patterns even without labeled data Representative methods: • K-means clustering • Hierarchical clustering • DBSCAN (density-based clustering) • PCA (Principal Component Analysis) • Autoencoder • ICA (Independent Component Analysis) • PCA-based anomaly detection models Advantages of unsupervised learning: • Applicable to large volumes of data without the need for labeled samples • Capable of discovering hidden structures and features, enabling new insights • Serves as an important step for data preprocessing and subsequent learning stages However, because "correctness" cannot be explicitly defined, evaluation metrics and interpretation can be challenging. In the era of big data, unsupervised learning is an indispensable technology for exploratory data analysis and the foundation of autonomous AI learning — with applications expanding across industries and research fields.