Unsupervised learning is a type of machine learning where the algorithm is left to find patterns and insights from the data on its own. It is a powerful tool for exploring and understanding complex datasets.

Key Concepts

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

Common Algorithms

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Association Rules (Apriori, Eclat)

Real-World Applications

  • Market Segmentation
  • Anomaly Detection
  • Recommendation Systems

Clustering Example

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