Question

Text documents are often grouped based on their content similarity to efficiently organize and categorise them for information retrieval systems and recommendation engines.
Identify and define the unsupervised learning method utilized in this context.

Hint:

Clustering is unsupervised — it finds hidden patterns without using labelled data.

Answer 1

Grouping text documents by similarity using an unsupervised method is known as clustering.
This technique aggregates similar data points without requiring prior labels.
For text data, algorithms such as K-Means or Hierarchical Clustering place documents with related content into distinct clusters.
This capability is vital for automatically organizing extensive text datasets for applications like search engines, topic modeling, and recommendation systems.
By assigning similar documents to the same group, clustering enhances information retrieval and user access to relevant content.
It represents a core technique within Natural Language Processing (NLP) and unsupervised machine learning.