NLP Lesson 30 – Document Clustering | Dataplexa

Document Clustering

In the previous lessons, you learned how to:

Convert text into vectors (TF-IDF)
Measure similarity using cosine similarity

Now we bring everything together using Document Clustering.

Document clustering allows machines to group similar documents automatically without knowing labels in advance.

What Is Document Clustering?

Document clustering is an unsupervised learning task where documents are grouped based on similarity.

Unlike classification:

No predefined categories
No labeled training data

The algorithm discovers structure directly from text data.

Why Document Clustering Is Important

Clustering is used when:

You do not know categories beforehand
You want to explore large text collections
You want automatic grouping

It is widely used in search engines, news aggregation, customer feedback analysis, and topic discovery.

Where Document Clustering Fits in NLP Pipeline

A standard clustering workflow looks like this:

Text collection
Text cleaning
Vectorization (TF-IDF / embeddings)
Similarity or distance computation
Clustering algorithm

Every step you learned earlier is required here.

Understanding K-Means Intuitively

K-Means works by:

Choosing K cluster centers
Assigning documents to nearest center
Updating cluster centers
Repeating until stable

The goal is to minimize within-cluster distance.

Why We Use TF-IDF for Clustering

Raw text cannot be clustered directly.

TF-IDF helps because:

Common words get low weight
Important words get higher weight
Document length bias is reduced

This leads to better clustering results.

Practical Demo: Document Clustering with K-Means

We now cluster documents into groups.

Where to run this code:

Google Colab (recommended)
Jupyter Notebook (Anaconda)

Python Example: Document Clustering

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

documents = [
    "I love machine learning",
    "Deep learning improves AI",
    "I enjoy playing football",
    "Football is a great sport",
    "Artificial intelligence is powerful"
]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(documents)

kmeans = KMeans(n_clusters=2, random_state=42)
kmeans.fit(X)

labels = kmeans.labels_

for doc, label in zip(documents, labels):
    print(label, ":", doc)

Understanding the Output

Each document is assigned a cluster label:

Cluster 0 → related documents
Cluster 1 → another topic group

You will notice:

AI-related documents group together
Sports-related documents group together

This happens without providing any labels.

How to Choose the Value of K

Choosing K is important.

Common approaches:

Domain knowledge
Elbow method
Silhouette score

For exams, remember:

K is user-defined