Clustering is one of the most practical ways to understand data. Among the different clustering methods, hierarchical clustering is widely used because it is simple, visual, and doesn’t require you to fix the number of clusters in advance.
Think of it like arranging your cupboard. Shirts go with shirts, trousers with trousers. Hierarchical clustering does the same but with data points, grouping similar ones together step by step.
How it works
Hierarchical clustering is an unsupervised learning technique. It builds a tree-like structure of clusters, known as a dendrogram. There are two main approaches:
Agglomerative (Bottom-Up): Start with every data point as its own cluster, then merge the closest ones.
Divisive (Top-Down): Start with one big cluster, then split it into smaller groups.
The dendrogram helps you decide how many clusters to keep by “cutting” it at different levels.
Applications in real life
Customer segmentation in marketing
Fraud detection in finance
Organising research papers or articles
Image segmentation in computer vision
Gene grouping in bioinformatics
Why learn this?
For students and beginners, hierarchical clustering is easy to pick up. It doesn’t need you to predefine clusters, gives clear visuals, and works well for small to medium datasets.
If you’re learning data science in India, try experimenting with Python libraries like SciPy and Scikit-learn to create your own dendrograms. It’s a simple way to build skills and confidence for real-world projects.
Top comments (0)