How Agglomerative Clustering Handles Outliers

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Clustering is a fundamental task in unsupervised learning, but one of the challenges it faces is handling outliers — data points that deviate significantly from the majority.

The Question

How does agglomerative clustering handle outliers?

Options:

  1. ❌ It ignores outliers during the clustering process.

  2. ✅ It assigns outliers to the nearest cluster.

  3. ❌ It creates separate clusters for outliers.

  4. ❌ It removes outliers from the dataset before clustering.

Correct Answer: It assigns outliers to the nearest cluster.

Why?

Agglomerative clustering is a hierarchical clustering method that builds clusters step by step:

  1. Start with each point as its own cluster.

  2. Iteratively merge the closest clusters.

  3. Continue until all points are grouped into a hierarchy (a dendrogram).

👉 Since agglomerative clustering does not have a built-in mechanism for identifying or removing outliers, every data point (even the outlier) eventually gets merged into some cluster.

  • Outliers are simply assigned to the nearest cluster based on distance metrics (like Euclidean, Manhattan, etc.).

  • This can distort clusters if outliers are extreme, because the linkage criterion (single, complete, or average linkage) may stretch the cluster boundaries unnaturally.

Visual Intuition

Imagine clustering customer purchase data:

  • Most customers cluster around typical spending habits.

  • A few customers have unusually high purchases (outliers).

Agglomerative clustering won’t separate these special customers — instead, it just pulls them into the nearest spending cluster, even if they don’t really belong.

Key Takeaway

  • ✅ Outliers in agglomerative clustering are not treated specially.

  • ❌ They are not ignored, not separated into their own clusters, and not removed automatically.

  • ⚠️ This makes hierarchical clustering sensitive to outliers, so preprocessing (outlier detection or scaling) is often necessary.

📌 In practice: If your dataset contains many outliers, consider preprocessing with methods like Z-score filtering, DBSCAN (which can label outliers), or robust scaling before applying agglomerative clustering.

Would you like me to also add a comparison between Agglomerative Clustering and DBSCAN (since DBSCAN can handle outliers explicitly)? That would make the blog even stronger.

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