23,October,2023

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.