Question

What is a significant application of unsupervised learning in anomaly detection?

• A. Identifying patterns in structured data
• B. Classifying data points into predefined categories
• C. Detecting unusual data points
• D. Predicting future outcomes

Hint:

Unsupervised learning = clusters + anomaly detection = find what doesn’t fit.

Answer 1

The Correct Option is C

Unsupervised learning algorithms work without labeled output data.
They find hidden patterns and structures in data, and one important application is anomaly detection.
Anomaly detection involves identifying data points that do not conform to the expected pattern — these could be errors, fraud, or outliers.
Option (A) is true in general but is broader than anomaly detection.
Option (B) is related to supervised learning since predefined categories mean labeled data.
Option (D) — predicting future outcomes — is a typical goal of supervised learning and forecasting models.
Therefore, detecting unusual data points is a key task done using unsupervised learning techniques like clustering or autoencoders.