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Clustering Basics - Beginner Friendly Guide
This notebook introduces unsupervised learning using clustering algorithms.
Unlike supervised learning, clustering does not use labels. Instead, the goal is to discover natural groupings within the data.
In this notebook we will:
- Generate a synthetic dataset
- Visualize the dataset
- Apply K-Means Clustering
- Use the Elbow Method to choose the best number of clusters
- Apply Hierarchical (Agglomerative) Clustering
- Apply DBSCAN Clustering
- Compare clustering results
Libraries used:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit‑learn
Import Libraries
Codepython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import DBSCAN
from sklearn.metrics import silhouette_score
sns.set_style("whitegrid")Generate Synthetic Dataset
We will generate a simple dataset with 2 features and 400 data points.
make_blobs() creates clusters automatically so we can test clustering algorithms.
Codepython
X, y = make_blobs(
n_samples=400,
centers=4,
cluster_std=1.2,
random_state=42
)
X.shapeOutput
(400, 2)Visualize the Dataset
Codepython
plt.figure(figsize=(6,5))
plt.scatter(X[:,0], X[:,1])
plt.title("Dataset Visualization")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.show()Output
K-Means Clustering
K-Means is one of the most commonly used clustering algorithms.
Steps:
- Choose number of clusters (K)
- Randomly initialize cluster centroids
- Assign each point to nearest centroid
- Update centroid positions
- Repeat until convergence
Train KMeans Model
Codepython
kmeans = KMeans(n_clusters=4, random_state=42)
kmeans.fit(X)
labels = kmeans.labels_Output
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
Visualize KMeans Clusters
Codepython
plt.figure(figsize=(6,5))
plt.scatter(X[:,0], X[:,1], c=labels, cmap="viridis")
plt.title("KMeans Clustering Result")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.show()Output
Elbow Method
The Elbow Method helps determine the optimal number of clusters.
We train KMeans with different values of K and measure:
Inertia (Within Cluster Sum of Squares)
The point where inertia stops decreasing sharply is the optimal K.
Codepython
inertia = []
k_values = range(1,10)
for k in k_values:
model = KMeans(n_clusters=k, random_state=42)
model.fit(X)
inertia.append(model.inertia_)Output
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
/opt/envs/ds/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
super()._check_params_vs_input(X, default_n_init=10)
Codepython
plt.figure(figsize=(6,5))
plt.plot(k_values, inertia, marker="o")
plt.title("Elbow Method")
plt.xlabel("Number of Clusters")
plt.ylabel("Inertia")
plt.show()Output
Hierarchical Clustering
Hierarchical clustering builds a tree-like cluster structure.
Agglomerative clustering works by:
- Treating each data point as its own cluster
- Iteratively merging closest clusters
Codepython
agg = AgglomerativeClustering(n_clusters=4)
agg_labels = agg.fit_predict(X)Codepython
plt.figure(figsize=(6,5))
plt.scatter(X[:,0], X[:,1], c=agg_labels, cmap="plasma")
plt.title("Agglomerative Clustering Result")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.show()Output
DBSCAN Clustering
DBSCAN groups points based on density.
Advantages:
- Can detect noise points
- Works well with arbitrary shaped clusters
Codepython
dbscan = DBSCAN(eps=0.7, min_samples=5)
db_labels = dbscan.fit_predict(X)Codepython
plt.figure(figsize=(6,5))
plt.scatter(X[:,0], X[:,1], c=db_labels, cmap="cool")
plt.title("DBSCAN Clustering Result")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.show()Output
Groupby aggregation with clustering
Codepython
import statsmodels.api as sm
import pandas as pd
# Load the mtcars dataset from R
mtcars_r = sm.datasets.get_rdataset('mtcars')
# Access the data as a pandas DataFrame
mtcars_df = mtcars_r.data
# Print the first few rows to verify
mtcars_df.head()
Output
mpg cyl disp hp drat wt qsec vs am gear \
rownames
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
carb
rownames
Mazda RX4 4
Mazda RX4 Wag 4
Datsun 710 1
Hornet 4 Drive 1
Hornet Sportabout 2 Codepython
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(mtcars_df)Codepython
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
inertia = []
K_range = range(1, 11)
for k in K_range:
kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
kmeans.fit(X_scaled)
inertia.append(kmeans.inertia_)
plt.figure(figsize=(6,4))
plt.plot(K_range, inertia, marker='o')
plt.xlabel("Number of Clusters (K)")
plt.ylabel("Inertia")
plt.title("Elbow Method for Optimal K")
plt.show()Output
Codepython
kmeans = KMeans(n_clusters=4, random_state=42, n_init=10)
clusters = kmeans.fit_predict(X_scaled)
mtcars_df["cluster"] = clustersCodepython
mtcars_df.head()Output
mpg cyl disp hp drat wt qsec vs am gear \
rownames
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
carb cluster
rownames
Mazda RX4 4 2
Mazda RX4 Wag 4 2
Datsun 710 1 3
Hornet 4 Drive 1 1
Hornet Sportabout 2 0 Codepython
pd.set_option('display.max_columns', 300)
Codepython
cluster_summary = mtcars_df.groupby("cluster").agg(["mean","sum","median","count"])
cluster_summaryOutput
mpg cyl disp \
mean sum median count mean sum median count mean
cluster
0 15.050000 180.6 15.20 12 8.000000 96 8.0 12 357.616667
1 20.742857 145.2 21.40 7 5.142857 36 6.0 7 175.114286
2 18.500000 92.5 19.70 5 6.800000 34 6.0 5 223.400000
3 28.075000 224.6 28.85 8 4.000000 32 4.0 8 93.612500
hp drat \
sum median count mean sum median count mean sum
cluster
0 4291.4 355.00 12 194.166667 2330 180.0 12 3.120833 37.45
1 1225.8 167.60 7 102.142857 715 105.0 7 3.570000 24.99
2 1117.0 160.00 5 198.800000 994 175.0 5 3.836000 19.18
3 748.9 87.05 8 81.875000 655 78.5 8 4.183750 33.47
wt qsec \
median count mean sum median count mean sum
cluster
0 3.075 12 4.104083 49.249 3.8100 12 17.142500 205.71
1 3.700 7 3.194286 22.360 3.2150 7 19.967143 139.77
2 3.900 5 3.001000 15.005 2.8750 5 15.616000 78.08
3 4.095 8 2.042250 16.338 2.0375 8 18.450000 147.60
vs am gear \
median count mean sum median count mean sum median count mean
cluster
0 17.350 12 0.000 0 0.0 12 0.0 0 0.0 12 3.000000
1 20.000 7 1.000 7 1.0 7 0.0 0 0.0 7 3.571429
2 15.500 5 0.000 0 0.0 5 1.0 5 1.0 5 4.600000
3 18.605 8 0.875 7 1.0 8 1.0 8 1.0 8 4.250000
carb
sum median count mean sum median count
cluster
0 36 3.0 12 3.083333 37 3.0 12
1 25 4.0 7 2.142857 15 2.0 7
2 23 5.0 5 5.200000 26 4.0 5
3 34 4.0 8 1.500000 12 1.5 8 Codepython
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
plt.figure(figsize=(7,5))
plt.scatter(X_pca[:,0], X_pca[:,1], c=mtcars_df["cluster"], cmap="viridis")
plt.xlabel("PCA Component 1")
plt.ylabel("PCA Component 2")
plt.title("K-Means Clusters (K=4) - PCA Visualization")
plt.colorbar(label="Cluster")
plt.show()Output
Codepython
import seaborn as sns
sns.pairplot(
mtcars_df,
hue="cluster",
palette="Set2",
diag_kind="kde"
)
plt.show()Output
Codepython
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