Unsupervised learning is a type of machine learning where the algorithm is given access to a large set of input data and must learn a function to map input to output without a specific instruction on what to do.
Key Concepts
- Clustering: Grouping similar data points together.
- Dimensionality Reduction: Reducing the number of features in a dataset while retaining the essential information.
- Association: Finding interesting relationships between variables in large databases.
Types of Unsupervised Learning
- Clustering: K-means, Hierarchical Clustering.
- Dimensionality Reduction: PCA (Principal Component Analysis), t-SNE.
- Association: Apriori, Association Rule Learning.
Real-World Applications
- Market Basket Analysis
- Image Compression
- Anomaly Detection
Clustering
For more information on unsupervised learning, you can visit our Machine Learning page.
To dive deeper into clustering algorithms, check out our Clustering Tutorial.