chapter
    Unsupervised Learning PYQs for GATE DA

    GATE DA Unsupervised Learning: 6 chapters, 5 previous year questions (19% of Machine Learning), 0 practice questions and one solved question from each chapter

    A question from this chapter

    Question 1
    2024 PYQ
    Euclidean distance based -means clustering algorithm was run on a dataset of 100
    points with . If the points and are both part of cluster 3, then which
    ONE of the following points is necessarily also part of cluster 3?
    Question 2
    2026 PYQ
    Let four points in three-dimensional space be:

    P1: [2, 3, −1], P2: [3, 1, 1], P3: [5, −2, 3] and P4: [3, 3, 3].

    Hierarchical Agglomerative Clustering is used to cluster the above points. If Manhattan Distance is used as the distance metric during clustering, which of the following options indicates the two points that will be merged first?
    Question 3
    2026 PYQ
    In the following table, the Task column lists a few tasks related to machine learning. The Algorithm column lists a few algorithms.
    Each entry “t” from the Task column is to be matched with an appropriate entry “a” from the Algorithm column such that the task “t” can be solved using the algorithm “a”. Denote such a match as t:a

    Task Algorithm
    T1 – Clustering A1 – Markov Chain Monte Carlo
    T2 – Classification A2 – K-Medoid
    T3 – Sampling A3 – Linear Discriminant Analysis
    T4 – Feature Extraction A4 – Naive Bayes

    Which of the following options is/are the correct matching(s)?
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    Unsupervised Learning PYQs for GATE DA

    GATE DA Unsupervised Learning: 6 chapters, 5 previous year questions (19% of Machine Learning), 0 practice questions and one solved question from each chapter.

    About Unsupervised Learning Previous Year Questions (PYQs)

    5 previous year questions from Unsupervised Learning in GATE DA, grouped by chapter with the exam year, answer key and step-by-step solution for each.

    Unsupervised Learning Weightage in GATE DA

    Unsupervised Learning accounts for 5 of 26 Machine Learning previous year questions in our bank (19%), about 1.7 per paper across 3 papers.

    Unsupervised Learning Chapter Matrix

    ChapterTopicsPYQsShare of unit PYQsPractice questions
    Chapter 1 — Unsupervised Learning00%0
    k-Means Clustering and Cluster Geometryk-Means Cluster Geometry and Voronoi Regions, k-Means Cluster Geometry and Voronoi Regions120%0
    Chapter 2 — Unsupervised Learning00%0
    Hierarchical Clustering and Linkage MethodsHierarchical Clustering Distance Metrics and First Merge, Hierarchical Clustering Distance Metrics and First Merge, Single and Complete Linkage with Dendrograms, Single and Complete Linkage with Dendrograms360%0
    Chapter 3 — Unsupervised Learning00%0
    Unsupervised Learning Algorithms and Task MatchingMachine Learning Task and Algorithm Matching, Machine Learning Task and Algorithm Matching120%0

    More from Machine Learning

    One Solved Question from Each Unsupervised Learning Chapter

    Question 1 · k-Means Clustering and Cluster Geometry · 2024 MCQ
    Euclidean distance based -means clustering algorithm was run on a dataset of 100
    points with . If the points and are both part of cluster 3, then which
    ONE of the following points is necessarily also part of cluster 3?
    1. A.

    2. B.

    3. C.

    4. D.

    Question 2 · Hierarchical Clustering and Linkage Methods · 2026 MCQ
    Let four points in three-dimensional space be:

    P1: [2, 3, −1], P2: [3, 1, 1], P3: [5, −2, 3] and P4: [3, 3, 3].

    Hierarchical Agglomerative Clustering is used to cluster the above points. If Manhattan Distance is used as the distance metric during clustering, which of the following options indicates the two points that will be merged first?
    1. A.

    2. B.

    3. C.

    4. D.

    Question 3 · Unsupervised Learning Algorithms and Task Matching · 2026 MSQ
    In the following table, the Task column lists a few tasks related to machine learning. The Algorithm column lists a few algorithms.
    Each entry “t” from the Task column is to be matched with an appropriate entry “a” from the Algorithm column such that the task “t” can be solved using the algorithm “a”. Denote such a match as t:a

    Task Algorithm
    T1 – Clustering A1 – Markov Chain Monte Carlo
    T2 – Classification A2 – K-Medoid
    T3 – Sampling A3 – Linear Discriminant Analysis
    T4 – Feature Extraction A4 – Naive Bayes

    Which of the following options is/are the correct matching(s)?
    1. A.

      T1:A4, T2:A3, T3:A1, T4:A2

    2. B.

      T1:A2, T2:A4, T3:A1, T4:A3

    3. C.

      T1:A3, T2:A4, T3:A1, T4:A2

    4. D.

      T1:A4, T2:A2, T3:A1, T4:A3