Unsupervised learning is a type of machine learning where the algorithms learn from data that is not labeled. It is a powerful technique that allows us to discover hidden patterns and structures in data without the need for explicit supervision.

Key Concepts

  • Clustering: This is the process of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
  • Association: This involves finding interesting relationships between variables in large databases.
  • Dimensionality Reduction: This is the process of reducing the dimensionality of the data while retaining as much of the original information as possible.

Types of Unsupervised Learning

  1. Clustering Algorithms:

    • K-Means
    • Hierarchical Clustering
    • DBSCAN

  2. Association Algorithms:

    • Apriori
    • Eclat

  3. Dimensionality Reduction Techniques:

    • Principal Component Analysis (PCA)
    • t-SNE

Use Cases

  • Market Basket Analysis: Used in retail to identify products that are frequently bought together.
  • Image Segmentation: Used in computer vision to divide an image into multiple segments.
  • Anomaly Detection: Used to identify unusual patterns that may indicate an error or fraud.

Clustering Example

For more information on unsupervised learning, check out our Introduction to Machine Learning.

If you are interested in learning more about machine learning, we recommend exploring our Machine Learning Resources.