Unsupervised learning is a machine learning technique where the model is trained on data that is not labeled. This type of learning is useful for finding hidden patterns in data and can be applied to various applications. In this section, we will explore some of the applications of unsupervised learning.

Common Applications

  • Clustering: Grouping similar data points together.
  • Association Rules: Discovering interesting relationships between variables in large databases.
  • Dimensionality Reduction: Reducing the number of variables in a dataset.
  • Anomaly Detection: Identifying unusual patterns that may indicate an error or fraud.

Real-World Examples

  • Market Basket Analysis: Understanding customer buying patterns to create targeted marketing strategies.
  • Recommendation Systems: Personalizing recommendations for products or content.
  • Image Compression: Reducing the size of images while preserving their quality.
  • Anomaly Detection in IoT: Identifying unusual activities in IoT devices, such as security breaches.

Further Reading

For more information on unsupervised learning, you can visit our Machine Learning Basics page.

[center]Clustering

[center]Association Rules

[center]Dimensionality Reduction

[center]Anomaly Detection