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DBSCAN

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Density-Based Spatial Clustering of Applications with Noise (DBSCAN) implementation in Python.

API inspired by Scikit-learn.

Usage

import numpy as np

from dataviz import generate_clusters
from dataviz import plot_clusters
from dbscan import DBSCAN

def generate_data(num_clusters: int, seed=None) -> np.ndarray:
    num_points = 20
    spread = 7
    bounds = (1, 100)
    clusters = generate_clusters(num_clusters, num_points, spread, bounds, bounds, seed)
    return np.array(clusters)

num_clusters = 4
clusters = generate_data(num_clusters, seed=1)
dbscan = DBSCAN(eps=7, min_samples=5)
dbscan.fit(clusters)
plot_clusters(clusters, dbscan.labels_, dbscan.components_)

dbscan plot

  • Red crosses denote core points

Reference: Introduction to Data Mining (1st Edition) by Pang-Ning Tan Section 8.4, Page 526

Original Paper: Ester, Martin, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. "A density-based algorithm for discovering clusters in large spatial databases with noise." In Kdd, vol. 96, no. 34, pp. 226-231. 1996.

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