Introduction to Unsupervised Learning: Clustering, Dimensionality Reduction & More
- Aryan
- Sep 22
- 2 min read
INTRODUCTION TO UNSUPERVISED LEARNING
Unsupervised learning is a type of machine learning (ML) where we do not have output values or labels—only input features or raw data.
The goal is to uncover hidden patterns, structures, or relationships within the data without predefined answers.
Unsupervised learning can be broadly classified into several categories:
Clustering
Association Rule Learning
Dimensionality Reduction
Anomaly Detection
Language Modeling
Clustering
Clustering is the task of grouping unlabeled data into clusters of similar items.
Since we don’t have labels, the algorithm organizes data points into groups where members of the same group are more similar to each other than to those in other groups.
This is why the process is called clustering.
Association Rule Learning
Imagine you are working as a manager in a supermarket and need to arrange 5,000 products on shelves.
You cannot place them randomly—products are usually organized logically based on customer buying patterns.
For example, after analyzing supermarket bills, you might find that 100 people purchased milk and 80 of them also bought eggs.
This shows a strong association between milk and eggs, so placing them near each other increases convenience and sales.
Two well-known algorithms for association rule learning are Apriori and Eclat.
Dimensionality Reduction
In ML, each input column is called a feature.
Having too many features can lead to longer processing times, overfitting, and reduced algorithm performance.
Dimensionality reduction techniques aim to reduce the number of features while preserving important information.
These methods are also useful for data visualization in 2D or 3D.
Popular algorithms include PCA (Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding).
Anomaly Detection
Anomaly detection focuses on identifying outliers—data points that deviate significantly from the normal pattern.
This is useful in applications such as fraud detection or fault monitoring.
A commonly used algorithm is Isolation Forest.
Applications of Clustering
Clustering has a wide range of practical applications in data science and business. Some key examples include:
Customer Segmentation
This is one of the most important real-world uses of clustering.
Imagine you are a marketing manager at Myntra and need to promote a sale on high-end products.
Suppose the company has 5 crore (50 million) customers.
Instead of notifying every customer, you can cluster customers based on their buying behavior and target only those groups most likely to purchase high-end products.
This saves cost and improves the effectiveness of marketing campaigns.
Data Analysis
When dealing with very large datasets (e.g., millions of customer records), clustering helps to segment the data into meaningful groups.
Each cluster can then be studied separately to discover unique patterns or behaviors that might be hidden in the overall dataset.
Semi-Supervised Learning
Clustering can also support semi-supervised learning, where some data is initially unlabeled but later labeled using human input.
For example, in Google Photos, similar photos are first clustered together.
The system then asks the user to label one photo, and the label is propagated to the entire cluster.
This way, unlabeled data gradually becomes labeled.
Image Segmentation
In computer vision, clustering can be used for image segmentation, where an image is divided into different segments or regions.
Each segment is labeled or colored based on its attributes (such as texture, color, or intensity).
This is useful in applications like medical imaging, object detection, and autonomous driving.
