Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This means that the data does not have any labels or classifications associated with it. Instead, the algorithm tries to find patterns or structures in the data on its own.

Key Concepts

  • Clustering: Grouping similar data points together.
  • Dimensionality Reduction: Reducing the number of variables in a dataset.
  • Anomaly Detection: Identifying data points that do not conform to the expected patterns.

Applications

Unsupervised learning has a wide range of applications, including:

  • Market Basket Analysis: Finding patterns in customer purchasing behavior.
  • Image Recognition: Identifying objects in images.
  • Recommendation Systems: Suggesting items to users based on their preferences.

Further Reading

For more information on unsupervised learning, you can explore the following resources:

Unsupervised Learning Visualization