🟪 1-Minute Summary
K-Means partitions data into K clusters by minimizing within-cluster variance. Algorithm: (1) Initialize K centroids randomly, (2) Assign points to nearest centroid, (3) Update centroids, (4) Repeat until convergence. Choose K using elbow method or silhouette score. Pros: fast, scalable. Cons: need to specify K, assumes spherical clusters, sensitive to initialization and outliers.
🟦 Core Notes (Must-Know)
How K-Means Works
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Algorithm Steps
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Choosing K
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- Elbow method
- Silhouette score
Pros & Cons
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Assumptions
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“How does K-Means work? Walk me through the algorithm”
- [Answer: Initialize K centroids → assign points → update centroids → repeat]
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“What’s the elbow method?”
- [Answer: Plot inertia vs K, look for “elbow” point]
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“What are limitations of K-Means?”
- [Answer: Needs K, spherical clusters, sensitive to outliers, initialization]
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“What happens if you initialize K-Means poorly?”
- [Answer: Local optimum - use k-means++ initialization]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Not scaling features
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Mistake 2: Using K-Means for non-spherical clusters
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Mistake 3: Not handling outliers
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🟩 Mini Example (Quick Application)
Scenario
[Customer segmentation with K-Means]
Solution
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
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# Including elbow method visualization
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