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Hierarchical Clustering

HC Intuition: Understanding HC

Agglomerative HC

• STEP 1: Make each data point a single-point cluster → That forms N clusters

• STEP 2: Take the two closest data points and make them one cluster → That forms N-1 clusters

• STEP 3: Take the two closest clusters and make them one cluster → That forms N-2 clusters

• STEP 4: Repeat STEP 3 until there is only one cluster

• FIN

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Euclidean Distance

Euclidean Distance between P1 and P2 = √[(x2-x1)2 + (y2-y1)2]

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Distance Between Clusters

Distance Between Two Clusters:

• Option 1: Closest Points

• Option 2: Furthest Points

• Option 3: Average Distance

• Option 4: Distance Between Centroids

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Download Resources

• Clustering | Pros & Cons | Notebook
• Python Notebook
• Dataset
• Hierarchical Clustering | R Code

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