Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This tutorial will cover the basics of unsupervised learning, including common algorithms and their applications.
Common Unsupervised Learning Algorithms
- Clustering: Grouping data into clusters based on their similarity. Read more about clustering algorithms.
- Association: Finding interesting relationships between variables in large databases. Learn more about association rules.
- Dimensionality Reduction: Reducing the number of variables in a dataset while retaining most of the information. Explore dimensionality reduction techniques.
Applications of Unsupervised Learning
Unsupervised learning has a wide range of applications, including:
- Market Basket Analysis: Identifying patterns in customer purchasing behavior.
- Image Compression: Reducing the size of images while preserving their quality.
- Anomaly Detection: Identifying unusual patterns that could indicate fraud or errors.
Clustering Example
Conclusion
Unsupervised learning is a powerful tool for exploring and understanding data. By using unsupervised learning algorithms, you can uncover hidden patterns and relationships in your data that may not be apparent through traditional analysis methods.