Hierarchical Clustering

Hierarchical Clustering is one of the Unsupervised Learning

Steps:

1. similar_ones = find 2 most similar
2. Merge them together
3. If length is greater than 1, go to Step 1

• For Step 1, we can use different metrics
  • Manhattan Distance
  • Euclidian Distance
• For step 2, we can use Average or Weighted Average or any other metrics
• In the final diagram, height gives the sense of similarity
  • low height = most similar
  • max height = lowest similarity