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

Key Concepts

  • Clustering: Grouping data points into clusters based on their similarity.
  • Dimensionality Reduction: Reducing the number of variables in a dataset while retaining most of the information.
  • Association Rules: Finding interesting relationships between variables in large databases.

Applications

Unsupervised learning has a wide range of applications, including:

  • Market Basket Analysis: Identifying items that are frequently purchased together.
  • Image Compression: Reducing the size of images while retaining their quality.
  • Anomaly Detection: Identifying unusual patterns that could indicate fraud or errors.

Resources

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

Clustering Algorithms

Here are some popular clustering algorithms:

  • K-Means
  • Hierarchical Clustering
  • DBSCAN

K-Means Clustering

Hierarchical Clustering

DBSCAN