Q68.Marks: +2.0UGC NET Paper 2: Computer Science 2nd January 2026 Shift 1
Given below are two statements: one is labelled as Assertion A and the other is labelled as Reason R
Assertion A: Unsupervised learning algorithms are used for tasks like data summarization and exploratory data analysis.
Reason R: Unsupervised learning requires labelled dataset to find relationships and patterns in data.
In the light of the above statements, choose the most appropriate answer from the options given below
1.Both A and R are correct and R is the correct explanation of A
2.Both A and R are correct but R is NOT the correct explanation of A
3.A is correct but R is not correct✓ Correct
4.A is not correct but R is correct
Solution
The correct answer is A is correct but R is not correct.
Key Points
Unsupervised learning algorithms are used for tasks like clustering, data summarization, and exploratory data analysis.
Unsupervised learning does not require labelled datasets. Instead, it identifies patterns, relationships, or structures in the data based on inherent characteristics.
Examples of unsupervised learning algorithms include K-Means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Principal Component Analysis (PCA).
The Reason (R) given in the question is incorrect because it states that unsupervised learning requires labelled data, which contradicts the definition and purpose of unsupervised learning.
Additional Information
Key Characteristics of Unsupervised Learning:
Works with unlabelled datasets to identify patterns and groupings.
It is primarily used for exploratory data analysis and dimensionality reduction.
Helps in gaining insights into the data, which can further be used for supervised learning tasks.
Applications of Unsupervised Learning:
Clustering: Grouping data into clusters based on similarities (e.g., customer segmentation).
Anomaly Detection: Identifying outliers or anomalies in data (e.g., fraud detection).
Dimensionality Reduction: Reducing the number of features in data while preserving variance (e.g., PCA for visualization).
Difference Between Supervised and Unsupervised Learning:
Supervised learning requires labelled data and maps inputs to outputs, while unsupervised learning works with unlabelled data to find patterns.
Supervised learning is used for classification and regression, whereas unsupervised learning is used for clustering and association.
