A Comparison of Clustering Algorithms: K-Means, DBSCAN, and Hierarchical
Clustering is an unsupervised machine learning technique used to group similar data points. Three widely used algorithms are K-Means, DBSCAN, and Hierarchical Clustering. Each has unique strengths, weaknesses, and ideal use cases.
1. K-Means Clustering
How it Works
Divides data into K groups based on distance.
Assigns each point to the nearest cluster center (centroid).
Iteratively updates centroids until convergence.
Strengths
Simple and fast
Works well with large datasets
Efficient for spherical or well-separated clusters
Easy to understand and implement
Weaknesses
Requires choosing K in advance
Sensitive to initial centroids
Fails at detecting non-spherical clusters
Sensitive to noise and outliers
When to Use
Large datasets
Clusters are compact, round, and evenly sized
You can estimate the number of clusters
2. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
How it Works
Groups points based on density.
Points in high-density areas form clusters.
Points in low-density regions become outliers/noise.
Two parameters:
eps (neighborhood radius)
minPts (minimum points required to form dense region)
Strengths
Does not require K
Can identify arbitrary-shaped clusters
Robust to noise and outliers
Good for spatial data with varying density
Weaknesses
Choosing eps and minPts is tricky
Struggles when clusters have very different densities
Not ideal for very high-dimensional data
When to Use
Data with noise or outliers
Arbitrary-shaped clusters
Unknown number of clusters
Geospatial or IoT sensor datasets
3. Hierarchical Clustering
How it Works
Two types:
Agglomerative: Start with single points → merge clusters
Divisive: Start with one cluster → split it
Creates a dendrogram, a tree-like structure showing cluster relationships.
Strengths
No need to choose the number of clusters initially
Produces full clustering hierarchy (dendrogram)
Works well for small or medium-size datasets
Can use various distance measures (Euclidean, Manhattan, cosine)
Weaknesses
Computationally expensive for large datasets
Sensitive to noise and outliers
Once a merge/split happens, it cannot be undone (“greedy” process)
When to Use
Small datasets (<10,000 points)
Want a visual hierarchy (dendrogram)
Need flexible distance metrics
Clusters are not too large or noisy
4. Comparison Table
Feature K-Means DBSCAN Hierarchical
Requires number of clusters? Yes (K) No No (can cut dendrogram later)
Cluster shape Spherical Arbitrary Arbitrary
Handles noise/outliers Poor Excellent Poor
Computational cost Low Medium High
Works with large data Yes Yes (with careful tuning) Not ideal
Distance metric Usually Euclidean Any (density-based) Many choices
Detects non-convex clusters No Yes Sometimes
Interpretability Easy Moderate High (dendrogram)
5. Summary of Best Use Cases
Use K-Means when:
Data is well-behaved, largely spherical, and K is known.
Use DBSCAN when:
Data has noise, outliers, or irregular cluster shapes.
Use Hierarchical Clustering when:
Dataset is small or medium-sized and you want a cluster hierarchy.
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