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Data Science

May 2026×Notebook lesson

Notebook converted from Jupyter for blog publishing.

00-DBSCAN

Driptanil DattaSoftware Developer

Introduction to DBSCAN

Let's briefly explore visually the differences between DBSCAN and other clustering techniques, such as K-Means Clustering.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

blobs = pd.read_csv('../DATA/cluster_blobs.csv')

blobs.head()

HTML

sns.scatterplot(data=blobs,x='X1',y='X2')

RESULT

<AxesSubplot:xlabel='X1', ylabel='X2'>

PLOT

moons = pd.read_csv('../DATA/cluster_moons.csv')

moons.head()

HTML

sns.scatterplot(data=moons,x='X1',y='X2')

RESULT

<AxesSubplot:xlabel='X1', ylabel='X2'>

PLOT

circles = pd.read_csv('../DATA/cluster_circles.csv')

circles.head()

HTML

sns.scatterplot(data=circles,x='X1',y='X2')

RESULT

<AxesSubplot:xlabel='X1', ylabel='X2'>

PLOT

def display_categories(model,data):
    labels = model.fit_predict(data)
    sns.scatterplot(data=data,x='X1',y='X2',hue=labels,palette='Set1')

from sklearn.cluster import KMeans
model = KMeans(n_clusters = 2)

display_categories(model,moons)

PLOT

model = KMeans(n_clusters = 3)
display_categories(model,blobs)

PLOT

model = KMeans(n_clusters = 2)
display_categories(model,circles)

PLOT

from sklearn.cluster import DBSCAN

model = DBSCAN(eps=0.6)

display_categories(model,blobs)

PLOT

model = DBSCAN(eps=0.15)
plt.figure(figsize=(10,6),dpi=150)
display_categories(model,moons)

PLOT

display_categories(model,circles)

PLOT

Let's further explore DBSCAN Hyperparameters!