Hierarchical Clustering In Machine Learning: Ever found yourself wondering what is hierarchical clustering in machine learning and why data scientists keep talking about it like it’s the secret sauce to solving real-world problems? You’re not alone. This method of clustering is like organising your messy wardrobe—grouping similar clothes together so you can find them easily later. Only here, we’re dealing with data.
This blog by Ze Learning Labb will walk you through hierarchical clustering in machine learning, its types, benefits, real-world uses, and why it’s such a big deal in today’s AI-driven world—especially if you’re learning data science in India.
In this blog, we’ll talk about hierarchical clustering in machine learning—what it is, how it works, its types, and why it matters. We’ll use easy language and examples that make sense even if you’re just starting out. We’ll cover key concepts like “hierarchical vs non-hierarchical clustering”, and answer FAQs like “Is hierarchical clustering supervised or unsupervised?”, and “What is the use of hierarchical clustering?”
You’ll also discover how this concept is relevant in India’s growing data science and analytics market—and we’ll connect it with Ze Learning Labb’s skill-building courses in Data Science, Data Analytics, and Digital Marketing to help you level up!
Let’s get cracking, shall we?
What is Hierarchical Clustering in Machine Learning?
To put it simply, hierarchical clustering in machine learning is a method used to group similar data points together based on how closely related they are. Think of it like building a family tree—but instead of relatives, you’re linking together data points.
There are no fixed number of groups or “clusters” at the beginning. The algorithm builds them step by step, either by:
- Starting with individual points and grouping them (bottom-up)
- Starting with one big group and splitting it (top-down)
This is very different from other clustering techniques like K-Means, which need you to specify how many groups you want beforehand.
“Hierarchical clustering builds a multilevel hierarchy of clusters, offering a more intuitive view of the dataset structure.” — Jain et al., Data Mining Handbook
Is Hierarchical Clustering Supervised or Unsupervised?
One common question is: Is hierarchical clustering supervised or unsupervised? The answer is, it’s unsupervised. That means it doesn’t rely on labelled data. It works purely based on the similarity or distance between data points.
This is great when you’re exploring data you know very little about—perfect for analysts, marketers, and researchers alike.
Types of Hierarchical Clustering
Understanding the types of hierarchical clustering is key to mastering it. There are mainly two:
1. Agglomerative Clustering (Bottom-Up)
This is the most commonly used form. Here’s how it works:
- Each data point starts in its own cluster.
- The algorithm keeps merging the two closest clusters.
- This continues until there’s only one big cluster or some stopping condition is met.
This type is simple and visual—great for beginners or students learning in Ze Learning Labb’s Data Science course.
2. Divisive Clustering (Top-Down)
The opposite of agglomerative:
- Start with one large cluster containing all data points.
- Then, divide it into smaller clusters.
- Keep dividing until each point stands alone or another rule is met.
Although less common in practice, it’s still important to understand, especially for theory exams or interviews.
How Does It Work?
Here’s a simplified breakdown:
- Compute the distance (using Euclidean or Manhattan) between every pair of data points.
- Link the closest points using a linkage method (like single linkage, complete linkage, or average linkage).
- Build a dendrogram—a tree-like diagram that shows how the clusters are formed.
Dendrograms: Visualising Clusters
A dendrogram helps us decide how many clusters to choose. Just slice the tree at a certain height, and you’ve got your clusters.
This technique is often taught practically in Ze Learning Labb’s Data Analytics classes using real-life datasets.
What is the Use of Hierarchical Clustering?
Wondering what is the use of hierarchical clustering in real-life? Here are some everyday applications:
- Customer segmentation in marketing
- Grouping genes in bioinformatics
- Organising news articles based on topic
- Detecting fraud in financial transactions
- Image segmentation in computer vision
Even in Digital Marketing, marketers use hierarchical clustering to identify similar customer behaviours and build targeted campaigns.
“Cluster analysis helps in dividing and conquering the market, leading to more focused and effective strategies.” — McKinsey Digital Report 2023
Hierarchical vs Non-Hierarchical Clustering
Now you might be asking—what’s the difference between hierarchical and non-hierarchical clustering?
| Features | Hierarchical Clustering | Non-Hierarchical (e.g. K-Means) |
| Clusters Predefined? | No | Yes |
| Process | Builds a tree structure | Iterative optimization |
| Visual Interpretation | Easier (using dendrogram) | Harder without visual tools |
| Suitable For | Small to medium datasets | Large datasets |
| Flexibility | High (no fixed clusters) | Low (fixed number of clusters) |
So, if you’re working on a smaller dataset or want to visually explore relationships, hierarchical clustering in ml is the way to go.
Advantages of Hierarchical Clustering
Why should anyone bother learning this? Let’s break down the advantages of hierarchical clustering:
- No need to predefine clusters
- Dendrograms provide clear visual insights
- Works well with small to medium data
- Simple logic and easy to understand
- Better performance on structured data
In Ze Learning Labb’s courses, students often build mini-projects using this technique—giving them real confidence.
All of this is taught in Ze Learning Labb’s Digital Marketing course, which links marketing analytics with real data tools.
Real-World Tip
Want to try this out? Use Python’s SciPy or Scikit-learn libraries to build a dendrogram and visualise clusters in minutes.
from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt
data = [[1, 2], [2, 3], [5, 8], [6, 9]]
linked = linkage(data, ‘single’)
dendrogram(linked)
plt.show()
Give this a go in a Google Colab notebook—it’s free, and a great way to practise.
On A Final Note…
So, to sum it all up—hierarchical clustering in machine learning is an unsupervised technique used to group similar data points together. It’s visual, intuitive, and super handy for small to medium datasets.
By now, you should know:
- What is hierarchical clustering
- Types of hierarchical clustering
- Advantages of hierarchical clustering
- The use of hierarchical clustering
- And how it stacks up against non-hierarchical methods
Whether you’re just starting out or brushing up your skills, hierarchical clustering is a valuable tool in your ML toolbox.
Want to actually learn by doing? Check out Ze Learning Labb’s practical and beginner-friendly courses in:
Each course includes hands-on projects and mentorship to help you grasp concepts like clustering from day one.
FAQs
1. Is hierarchical clustering in machine learning used in India?
Yes, absolutely! Many Indian startups and MNCs are using this for data segmentation and pattern detection.
2. What’s the best tool to learn hierarchical clustering in ml?
Python with libraries like Scikit-learn and Seaborn. Ze Learning Labb covers these in detail.
3. Which industries use this most?
Marketing, finance, healthcare, and e-commerce.
4. Is hierarchical clustering slow?
It can be slower for very large datasets. That’s why it’s ideal for small to medium data sizes.
5. Can I use hierarchical clustering for unstructured data?
Yes, especially with good feature extraction. Text clustering is a great example.