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DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that groups together points that are closely packed and marks points in low-density regions as noise. It is well-suited for datasets with arbitrary-shaped clusters and outliers.
Steps to Implement DBSCAN in Python
1. Import Required Libraries
import numpy as npimport matplotlib.pyplot as pltfrom sklearn.cluster import DBSCANfrom sklearn.datasets import make_blobsfrom sklearn.preprocessing import StandardScalerCopied!âś•Copy2. Create or Load Data
X, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.5, random_state=0)X = StandardScaler().fit_transform(X)Copied!âś•CopyScaling ensures features contribute equally to distance calculations.
3. Apply DBSCAN Clustering
db = DBSCAN(eps=0.3, min_samples=5).fit(X)labels = db.labels_Copied!âś•Copyeps: Maximum distance between two samples to be considered neighbors.
min_samples: Minimum number of points to form a dense region.
4. Visualize the Clusters
DBSCAN — scikit-learn 1.8.0 documentation
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