šŖ 1-Minute Summary
Hierarchical clustering builds a tree (dendrogram) showing how data points group together at different similarity levels. Two types: Agglomerative (bottom-up: merge) and Divisive (top-down: split). Advantage: don’t need to specify K beforehand, dendrogram visualizes structure. Disadvantage: slow for large datasets. Cut the dendrogram at desired height to get clusters.
š¦ Core Notes (Must-Know)
What is Hierarchical Clustering?
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Agglomerative vs Divisive
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How to Read a Dendrogram
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Linkage Methods
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- Single linkage
- Complete linkage
- Average linkage
- Ward linkage
When to Use
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šØ Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“What’s the advantage of hierarchical clustering over K-Means?”
- [Answer: Don’t need to specify K, shows clustering at all levels]
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“How do you decide where to cut the dendrogram?”
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“What’s the time complexity of hierarchical clustering?”
- [Answer: O(nĀ³) - slow for large datasets]
š„ Common Mistakes (Traps to Avoid)
Mistake 1: Using hierarchical clustering on very large datasets
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Mistake 2: Not choosing appropriate linkage method
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š© Mini Example (Quick Application)
Scenario
[Small dataset hierarchical clustering]
Solution
from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt
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