Clustering in Machine Learning: Types, Algorithms, Examples, and How It Works

Quick Overview:

• Clustering in machine learning is an unsupervised learning technique that groups similar data points into clusters without using labelled data.

• The main types include partitioning, hierarchical, density-based, model-based, and centroid-based clustering, for different data structures and requirements.

• Popular clustering algorithms include K-Means, hierarchical clustering, DBSCAN, and Gaussian Mixture Models, which use different methods to form clusters.

• This blog covers clustering in machine learning in detail along with its working, its types, Python implementation, advantages, limitations, and real-world applications.

What Is Clustering in Machine Learning?

Clustering in Machine Learning is a type of unsupervised machine learning algorithm which groups similar data points into clusters based on their different characteristics. Unlike supervised algorithms such as logistic regression or decision trees, clustering does not rely on labeled data at all. 

There is no known "correct answer" for each observation going in. Instead, the clustering algorithm looks purely at the structure of the data itself and organizes it based on similarity. 

Let’s understand this through a clustering in machine learning diagram below:

To make the topic even more clear let’s understand the clustering in machine learning by an analogy below:

Imagine you have a basket filled with different fruits, but none of them are labeled. You start observing their features. 

• You notice that some fruits are round and red.

• Some are long and yellow, while others are small and orange. 

Based on these similarities, you naturally place similar fruits into separate groups. 

You were not told which fruit belongs to which group. You identified the groups by looking at their similarities. This is how clustering works.

Also Read: Linear Regression in Machine Learning: Types, Algorithm, and Implementation

Now let’s explore how this clustering in machine learning works.

How Does Clustering in Machine Learning Work?

The clustering process identifies similarities between data points and assigns similar observations to the same cluster. Different clustering algorithms in machine learning use different approaches to measure similarity and create these groups.

The general process involves the following steps:

1. Collect the data: Start with a dataset which contains the relevant features.

2. Prepare the data: Clean the dataset and handle missing values or inconsistent observations.

3. Select features: Choose the variables that will be used to identify similarities between observations.

4. Choose a clustering algorithm: Select an appropriate method based on the dataset and objective.

5. Calculate similarity or distance: The clustering algorithms in machine learning​ determine how close or similar different observations are.

6. Create clusters: The similar observations are then grouped together.

7. Evaluate the results: Examine whether the resulting clusters are meaningful and useful.

For example, K means clustering in machine learning assigns observations to nearby cluster centres and repeatedly updates those centres until the groups stabilise.

Now let’s look at the different clustering in machine learning types to understand the clustering more clearly.

Also Read: Supervised vs Unsupervised Learning: Differences, Examples, and Applications

Types of Clustering in Machine Learning

There are different types of clustering in Machine Learning, and each type uses a different approach to group similar data points.

Let’s explore each of them in detail.

1. Partitioning Clustering

Partitioning clustering divides a dataset into a fixed number of non-overlapping groups, where every data point belongs to exactly one cluster. The number of clusters, usually called K, has to be specified in advance, and the algorithm then works to find the partition of the data that best satisfies its internal objective.

K Means clustering in machine learning is a common example of partitioning clustering. It requires the number of clusters to be specified before the algorithm is trained.

• Best for: Customer segmentation, market analysis, and grouping products.

2. Hierarchical Clustering in Machine Learning​

Hierarchical clustering in machine learning​ creates a hierarchy of clusters by progressively combining similar observations or dividing larger groups into smaller ones.

The resulting hierarchy can be represented using a tree-like diagram called a dendrogram, which helps visualise relationships between observations and determine suitable cluster groupings.

• Best for: Biological data analysis, document grouping, customer segmentation, and small to medium-sized datasets.

3. Density-Based Clustering

Density-based clustering groups data points based on how closely they are packed together. It looks for areas with many nearby data points and treats these as clusters.

Unlike methods that expect clusters to be round or circular, density-based methods such as DBSCAN can find clusters of different shapes. They can also identify data points that are far away from other points as noise or outliers instead of forcing them into a cluster.

• Best for: Spatial data, anomaly detection, and datasets containing irregularly shaped clusters.

4. Model-Based Clustering

Model-based clustering is a types of clustering in machine learning​ that assumes that the data comes from different underlying statistical groups. The algorithm builds a model for these groups and calculates how likely each data point is to belong to each one.

Gaussian Mixture Model (GMM) is a common example of model-based clustering. Unlike methods that simply assign a data point to the nearest cluster centre, GMM gives each point a probability of belonging to different clusters.

• Best for: Probabilistic clustering, statistical modelling, and datasets with overlapping groups.

5. Centroid-Based Clustering

Centroid-based clustering groups data points based on their distance from a central point called a centroid. Each data point is assigned to the centroid that is closest to it.

K Means clustering in machine learning is the most common example. It starts with some centroids, assigns data points to their nearest centroid, then moves the centroids based on the assigned points. This process continues until the centroids and clusters become stable.

• Best for: Numerical datasets where clusters can be represented effectively using central points.

Now after discussing the different types of clustering in machine learning, let's explore what major clustering algorithms in machine learning are available for different use cases.

Also Read: Random Forest Algorithm in Machine Learning: Working, Types, Pseudocode

Major Clustering Algorithms in Machine Learning

Different clustering algorithms in machine learning are designed for different types of datasets and clustering requirements. Some focus on distance from a centre, while others identify hierarchical relationships, density patterns, or probability distributions.

Let’s understand each of them in detail.

1. K-Means Clustering

K means clustering in machine learning is a centroid-based, partitioning algorithm that groups data into a specified number of clusters, K, by repeatedly assigning points to their nearest centroid and then recalculating each centroid as the mean position of the points assigned to it. 

It is fast and scales well to large datasets, and tends to work especially well when clusters in the data are roughly spherical and similarly sized. 

2. DBSCAN

DBSCAN, short for Density-Based Spatial Clustering of Applications with Noise, is the most widely used density-based clustering algorithms in machine learning​. It groups together points that are closely packed, based on two parameters, eps, the maximum distance between two points for them to be considered neighbors, and min_samples, the minimum number of neighboring points required for a region to count as dense. 

Unlike K means clustering, DBSCAN does not require the number of clusters to be specified in advance, and it can naturally identify outliers as noise points that do not belong to any cluster at all.  

3. Gaussian Mixture Model

A Gaussian Mixture Model, or GMM, is a model-based clustering algorithm that assumes the data was generated from a mixture of several Gaussian distributions, each representing one cluster. Rather than assigning each point to exactly one cluster the way K means clustering does, a GMM calculates the probability that each point belongs to each cluster, a concept known as soft clustering. 

This makes GMM a good fit for datasets where cluster boundaries genuinely overlap, or where understanding the uncertainty of a cluster assignment matters as much as the assignment itself.

Also Read: Gradient Boosting Algorithm in Machine Learning: How It Works, and Practical Examples

Now let’s understand how to perform clustering in machine learning using Python.

How to Perform Clustering in Machine Learning Using Python?

In this section you will walk through a complete clustering in machine learning python example using scikit-learn, comparing K-means clustering, hierarchical clustering, DBSCAN, and a Gaussian Mixture Model on the same dataset.

1. Load and Explore the Dataset

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.preprocessing import StandardScaler

from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN

from sklearn.mixture import GaussianMixture

from sklearn.metrics import silhouette_score

np.random.seed(42)

n = 300

cluster1 = np.random.normal(loc=[20, 30000], scale=[3, 5000], size=(n // 3, 2))

cluster2 = np.random.normal(loc=[35, 60000], scale=[4, 8000], size=(n // 3, 2))

cluster3 = np.random.normal(loc=[50, 90000], scale=[5, 10000], size=(n // 3, 2))

X = np.vstack([cluster1, cluster2, cluster3])

df = pd.DataFrame(X, columns=["Age", "AnnualIncome"])

print(df.head())

print(df.describe())

This creates a sample dataset of 300 customers spread across three natural age and income groupings, the same kind of customer segmentation scenario used throughout this series.

Also Read: Support Vector Machine (SVM) in Machine Learning: Algorithm, Types, Kernels, and Examples

2. Prepare and Scale the Data

scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)

Scaling matters for every distance-based clustering algorithm, since features on very different numeric scales, like age and annual income here, would otherwise distort how "close" two points really are to each other.

3. Select a Clustering Algorithm

kmeans = KMeans(n_clusters=3, init="k-means++", n_init=10, random_state=42)

hierarchical = AgglomerativeClustering(n_clusters=3, linkage="ward")

dbscan = DBSCAN(eps=0.25, min_samples=5)

gmm = GaussianMixture(n_components=3, random_state=42)

Each algorithm is set up to look for three clusters, except DBSCAN, which discovers the number of clusters on its own based on density rather than being told a target number directly.

4. Train the Clustering Model

kmeans_labels = kmeans.fit_predict(X_scaled)

hierarchical_labels = hierarchical.fit_predict(X_scaled)

dbscan_labels = dbscan.fit_predict(X_scaled)

gmm_labels = gmm.fit_predict(X_scaled)

5. Evaluate the Clusters

print("K-Means silhouette score:", silhouette_score(X_scaled, kmeans_labels))

print("Hierarchical silhouette score:", silhouette_score(X_scaled, hierarchical_labels))

n_clusters_db = len(set(dbscan_labels)) - (1 if -1 in dbscan_labels else 0)

n_noise = list(dbscan_labels).count(-1)

mask = dbscan_labels != -1

print("DBSCAN clusters found:", n_clusters_db, "| noise points:", n_noise)

print("DBSCAN silhouette score (excluding noise):", silhouette_score(X_scaled[mask], dbscan_labels[mask]))

print("GMM silhouette score:", silhouette_score(X_scaled, gmm_labels))

Running this comparison on the sample dataset produces silhouette scores of roughly 0.66 for K means clustering, 0.65 for hierarchical clustering in machine learning, and 0.66 for the Gaussian Mixture Model, all very close to each other since the underlying data genuinely contains three well-separated groups. DBSCAN, with its parameters tuned for this dataset, correctly finds 3 clusters while also flagging 14 points as noise, a result none of the other three algorithms are able to produce, since they force every point into a cluster regardless of how well it actually fits.

6. Visualise the Results

fig, axes = plt.subplots(1, 3, figsize=(18, 5))

axes[0].scatter(df["Age"], df["AnnualIncome"], c=kmeans_labels, cmap="viridis")

axes[0].set_title("K-Means Clustering")

axes[1].scatter(df["Age"], df["AnnualIncome"], c=hierarchical_labels, cmap="viridis")

axes[1].set_title("Hierarchical Clustering")

axes[2].scatter(df["Age"], df["AnnualIncome"], c=dbscan_labels, cmap="viridis")

axes[2].set_title("DBSCAN (noise in dark purple)")

for ax in axes:

    ax.set_xlabel("Age")

    ax.set_ylabel("Annual Income")

plt.tight_layout()

plt.show()

Expected Output:

Plotting all three side by side makes the practical differences between these clustering algorithms in machine learning immediately visible: K means clustering in machine learning and hierarchical clustering produce very similar, clean partitions on this dataset, while DBSCAN's output includes a distinct set of noise points that neither of the other two algorithms identify.

Also Read: K Nearest Neighbors Algorithm in Machine Learning: How It Works and Examples

Advantages and Limitations of Clustering

Clustering in machine learning is a genuinely useful tool for exploring data, but like every technique, it comes with real pros and cons.

Let’s explore these in detail.

Advantages of Clustering in Machine Learning

• Discovers hidden patterns: Helps identify natural groupings in unlabeled data.

• Useful for segmentation: Can group customers, products, or other observations based on similarities.

• Supports exploratory analysis: Helps researchers understand the underlying structure of a dataset.

• Wide range of applications: Clustering can be applied across marketing, healthcare, finance, education, and image processing.

• Multiple algorithm choices: Different methods can handle different cluster shapes, densities, and data structures.

Limitations of Clustering Algorithms

• Choosing the right algorithm can be difficult: Different datasets may require different clustering approaches.

• Some methods require predefined parameters: For example, K-Means requires the number of clusters in advance.

• Sensitive to outliers: Extreme observations can affect some clustering methods significantly.

• Feature scaling can matter: Distance-based methods can be influenced by differences in feature ranges.

• Clusters may not always have a clear meaning: Mathematical groupings may not necessarily represent useful real-world categories.

Now let’s talk about some real world applications of clustering in machine learning​.

Real-World Applications of Clustering in Machine Learning

Clustering in machine learning is used across a wide range of industries wherever discovering natural groupings in unlabeled data provides real analytical value. The key applications of clustering are:

Conclusion

As we now know, clustering in machine learning provides a practical way to discover patterns and group similar observations without predefined labels. From K-Means and hierarchical clustering to DBSCAN and Gaussian Mixture Models, different approaches can address different data structures and analytical requirements.

Understanding types of clustering in machine learning​, algorithms, Python implementation, advantages, limitations, and applications can help you choose an appropriate approach for real-world data analysis and pattern discovery.