Hierarchical Clustering

  • A clustering method that creates a tree of clusters. It’s useful if you want to understand hierarchical relationships between the clusters.
  • Steps:
    1. Treat each data point as a single cluster. Hence, if there are ‘N’ data points, we have ‘N’ clusters at the start.
    2. Merge the two closest clusters.
    3. Repeat step 2 until only one cluster remains.
  • Types of Hierarchical Clustering:
    • Agglomerative: This is a “bottom-up” approach. Initially, each point is considered a separate cluster, and then they are merged based on similarity.
    • Divisive: A “top-down” approach. Start with one cluster and divide it until each data point is a separate cluster.
  • Dendrogram: A tree-like diagram that showcases the arrangement of the clusters produced by hierarchical clustering.
  • Applications: Phylogenetic trees, sociological studies.
  • Discussion & Exercises:
  1. Compare and contrast K-means and Hierarchical Clustering.
  2. Explore various linkage methods in hierarchical clustering: Single, Complete, Average, and Ward.

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