# Clustering Algorithms in Machine Learning — A Beginner’s Guide

Clustering is a key part of machine learning that helps us **find patterns in unlabelled data**. Unlike supervised learning (where data comes with labels), clustering works without any labels. It simply groups similar data points together.

This method is used in many real-world applications — **customer segmentation in marketing, fraud detection in banking, document organisation, and even grouping similar genes in biology**.

### What Are Clustering Algorithms?

A [clustering algorithm](https://learninglabb.com/clustering-algorithms-in-machine-learning/) collects data points and places them into groups called **clusters**. Points inside a cluster are similar to each other and different from points in other clusters.

Think about an online store. By analysing buying habits, it can group customers as **regular buyers, discount seekers, festive shoppers, and occasional visitors** — without knowing their personal details.

### Types of Clustering Algorithms

Here are some commonly used clustering techniques:

* **K-Means** – simple and fast for large datasets
    
* **Hierarchical** – builds a tree-like structure, good for small datasets
    
* **DBSCAN** – detects unusual shapes and handles noisy data
    
* **GMM** – assigns probabilities to clusters
    
* **Mean-Shift** – useful for clusters of different shapes
    

### How to Measure Performance

Since clustering has no labels, accuracy is checked using **Silhouette Score, Davies-Bouldin Index, and Adjusted Rand Index** instead of normal accuracy percentages.

### Wrap-Up

Clustering algorithms help you **explore raw data and discover hidden patterns**. With tools like **Scikit-learn in Python**, it’s easy for [students and beginners](https://learninglabb.com/) to start experimenting on real datasets.