DBSCAN Clustering from Scratch

HardClusteringDensity-Based ClusteringDBSCANNoise HandlingUnsupervised Learning

Implement the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm from scratch and understand density-based clustering concepts, including core points, border points, and noise points.

Problem:

Implement the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm from scratch and understand density-based clustering concepts, including core points, border points, and noise points.

Examples:

Input: X = make_moons(n_samples=200, noise=0.1)[0]
dbscan = DBSCAN(eps=0.3, min_samples=5)
labels = dbscan.fit_predict(X)
Output: Number of clusters: 2
Noise points: 8
Silhouette score: 0.71
DBSCAN correctly identifying moon-shaped clusters
Input: X = np.random.randn(300, 2)
dbscan = DBSCAN(eps=0.3, min_samples=5)
labels = dbscan.fit_predict(X)
Output: Clusters found in dense regions
Noise points in sparse areas
DBSCAN handling random data with noise

Constraints:

  • Must identify noise points (label -1)
  • Must handle different distance metrics
  • Must implement efficient neighbor search

Code Editorpython

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Output

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