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Hierarchical Clustering

📑 5 slides 👁 8 views 📅 2/25/2026
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Introduction to Hierarchical Clustering

Hierarchical clustering groups similar data points into clusters.

Introduction to Hierarchical Clustering
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Types of Hierarchical Clustering

  • Agglomerative: Bottom-up approach, starts with individual points.
  • Divisive: Top-down approach, starts with one cluster.
  • Agglomerative is more commonly used in practice.
Types of Hierarchical Clustering
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Distance Metrics Used

  • Euclidean distance: Straight-line distance between points.
  • Manhattan distance: Sum of absolute differences.
  • Cosine similarity: Measures angle between vectors.
Distance Metrics Used
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Applications in Real World

  • Customer segmentation for targeted marketing campaigns.
  • Gene expression analysis in biological research.
  • Document clustering for information retrieval systems.
Applications in Real World
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Advantages and Limitations

  • Advantage: No need to specify number of clusters beforehand.
  • Limitation: Computationally expensive for large datasets.
  • Best for small to medium datasets with clear hierarchy.
Advantages and Limitations
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