DCA71M6 & FUNDAMENTALS OF UNSUPERVISED LEARNING

Description

SESSION	FEB-MARCH 2026
PROGRAM	MASTER OF COMPUTER APPLICATIONS (MCA)
SEMESTER	III
course CODE & NAME	DCA71M6 & FUNDAMENTALS OF UNSUPERVISED LEARNING

 

SET – 1

 

Q.1(a). Discuss the role of unsupervised learning in modern AI systems. Explain how it contributes to data preprocessing, representation learning, foundation models, reinforcement learning, and autonomous systems. (5 Marks)

Q.1(b). Elaborate on the importance of probability models such as Bayesian inference and Maximum Likelihood Estimation (MLE) in unsupervised learning. (5 Marks)

Ans 1a.

Unsupervised learning is a branch of machine-learning that identifies patterns and patterns in information without the need for specific examples. It is now a fundamental element of modern AI and is contributing to several crucial areas of system development and design.

Contributions to Modern AI

When data processing non-supervised methods like clustering and dimensionality reduction can be utilized to normalise, clean, \

 

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Q.2. Compare K-Means, DBSCAN, and Agglomerative Clustering in terms of working principle, assumptions, strengths, and limitations. (10 Marks)

Ans 2.

The task of clustering involves connecting similar data points without the use of predefined labels. In the array of algorithms for clustering developed, K-Means, DBSCAN, and Agglomerative Clustering are among the most widely used. Each operates according to a distinct principle and works best with different types of data as well as demands.

K-Means Clustering

K-Means, a centrroid-based algorithm that partitions data into a specified amount of clusters K. It begins with randomly establishing K clusters and every data point is assigned close to its nearest centroid then recalculating its centroid using the median of the points assigned to it. The process continues until the assignments no more change. K-Means assumes that clusters are spherical, approximately equal in size

 

Q.3. Explain the concept of density-based clustering and justify why it is suitable for datasets containing noise and outliers. (10 Marks)

Ans 3.

Density-based clustering is a method to grouping data points that describes clusters as zones of high data point density that are separated from areas with lower density. As opposed to other methods that use centroids such as K-Means and other density-based algorithms, these don’t require clusters to have a defined dimension or shape. The most representative algorithm in this class is DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise.

Core Concepts of Density-Based

 

SET – 2

Q.4. Explain the concept of matrix factorisation and discuss its importance in unsupervised learning. (10 Marks)

Matrix factorisation is an algorithmic technique which breaks down a massive matrix into two or smaller matrices, whose products are similar to the initial. This technique reveals the pattern of the data in a concise and readable shape. In unsupervised learning, the matrix factorization is frequently used to aid in the reduction of dimensionality, feature extraction and recommendations systems that makes it one of the most powerful and extensively used tools within the

 

Q.5. Explain the challenges associated with high-dimensional data in unsupervised learning. Discuss techniques such as Dimensionality Reduction (PCA, SVD) and their role in improving clustering performance. Illustrate with suitable examples. (10 Marks)

The term “high-dimensional” refers to data sets where every observation is defined by an enormous number of characteristics or variables. The data naturally arises in text analysis, genomics imaging, as well as sensor networks. While having more features may be appealing, a higher degree of dimensionality creates well-studied problems that significantly impact the performance of unsupervised learning algorithms, particularly clustering.

Challenges of High-Dimensional Data

The

 

 

Q.6. Explain the role of unsupervised learning in cybersecurity and healthcare. Illustrate how anomaly detection and pattern discovery are used for intrusion detection and disease diagnosis. Support your answer with relevant real-world examples and discuss challenges faced in these domains. (10 Marks)

Ans 6.

Unsupervised learning is particularly valuable in areas where labeled data is hard to come by, cost prohibitive to obtain, or where the pattern of interest is continually changing. Healthcare and cybersecurity are two examples of such areas. Both fields are supervised, and unsupervised learning powers anomaly detection and pattern detection that allows the early detection of threats and diseases, often before human experts are