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

The unsupervised learning method used for grouping text documents based on similarity is called clustering.
Clustering is a technique that groups similar data points together without any predefined labels.
In the context of text data, clustering algorithms like K-Means or Hierarchical Clustering group documents with similar content into clusters.
This helps in automatically organizing large collections of text for search engines, topic modeling, and recommendations.
By clustering, similar documents are placed in the same group, which improves information retrieval and makes it easier for users to find relevant information.
It is a fundamental method in Natural Language Processing (NLP) and unsupervised machine learning.