Unsupervised learning is a machine learning approach where the algorithm learns patterns from unlabeled data. Unlike supervised learning, it does not rely on predefined labels or outcomes. Instead, it focuses on discovering hidden structures or groupings within the dataset.
Key Concepts
- Objective: Identify underlying patterns, distributions, or relationships in data
- Common Techniques: Clustering, dimensionality reduction, association rule mining
- Applications: Customer segmentation, anomaly detection, data compression
Popular Algorithms
- 🧠 K-Means Clustering
- 📊 Principal Component Analysis (PCA)
- 🌐 DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Use Cases
- 📚 Document clustering for research papers
- 🧪 Anomaly detection in network traffic
- 🛠️ Feature engineering for predictive models
For deeper exploration, check our guide on unsupervised learning algorithms.