Artificial Intelligence & Machine Learning Basics

Unsupervised Learning

Artificial Intelligence & Machine Learning Basics 223 views Nov 05, 2025 1 min read

Unsupervised Learning is a type of Machine Learning where the model is trained on unlabeled data.
There are no predefined outputs — the system discovers patterns and structures on its own.

How Unsupervised Learning Works

  1. Provide raw, unlabeled data

  2. The algorithm analyzes similarities and differences

  3. Hidden patterns or groups are identified

  4. Useful insights are extracted

Types of Unsupervised Learning

1. Clustering

  • Groups similar data points together

  • Examples:

    • Customer segmentation

    • Grouping similar images

2. Association

  • Finds relationships between variables

  • Example:

    • Market basket analysis (items frequently bought together)

Common Algorithms

  • K-Means Clustering

  • Hierarchical Clustering

  • DBSCAN

  • Apriori Algorithm

  • Principal Component Analysis (PCA)

Applications

  • Customer segmentation

  • Anomaly and fraud detection

  • Recommendation systems

  • Image and document organization

Advantages

  • No need for labeled data

  • Can discover hidden patterns

Disadvantages

  • Results can be harder to interpret

  • Lower accuracy compared to supervised learning

Conclusion

Unsupervised Learning is useful when labeled data is unavailable and the goal is to explore data structure and relationships.

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