Clustering is one of the most interesting parts of machine learning. Unlike supervised learning, where data already has labels, clustering works with unlabelled data. It groups similar data points together, helping us find hidden patterns in large datasets.
You can see clustering used in many places — grouping customers based on shopping habits, finding fraud patterns in banking, organising large sets of documents, or grouping similar genes in biology.
What Is a Clustering Algorithm?
A clustering algorithm groups data points into clusters. Items inside one cluster are more similar to each other than to items in other clusters.
For example, an e-commerce platform can group its users into regular buyers, festive shoppers, and discount hunters just by studying their behaviour — without knowing their identities.
Popular Clustering Algorithms
Here are a few commonly used types:
K-Means – fast, simple, and works well for large datasets
Hierarchical – builds a tree-like structure, useful for smaller data
DBSCAN – handles noise and detects unusual shapes
GMM – gives probability-based cluster assignments
Mean-Shift – good for unevenly shaped clusters
Evaluating Clustering
Because clustering has no labels, accuracy is measured using metrics like Silhouette Score, Davies-Bouldin Index, and Adjusted Rand Index.
Final Note
Clustering is a simple way to explore patterns in raw data. With libraries like Scikit-learn in Python, students and beginners can easily start experimenting with clustering on real-world datasets.
Top comments (0)