Indian Institute of Technology Madras
BS Degree Programme · Examination & Study Paper
Machine Learning Techniques
K-means clustering
Examination Instructions & Guidelines
- Solve on notebook/paper first before checking solutions.
- MCQ: Exactly 1 option is correct. Negative marking applies.
- MSQ: One or more options correct. Select all to score.
- NAT: Numerical response. No negative marking.
- Scientific calculator allowed for all calculations.
- Official answer key is sealed at the end of this sheet.
Quiz 1 · 26 Oct 2025 (AN)Quiz 1
- Q1MSQ2 Marks·Choose all correct options
Which among the following is true with respect to the Lloyds’ algorithm?
- AIt always converges.
- BIt may fail to converge for an initialization while it may converge for other initialization.
- CIt is sensitive to outliers.
- DThe solution produced is always the optimal.
- Q2MSQ4 Marks·Choose all correct options
- AThe points (0, 2), (2, 0), and (2, 2) have the equal probability of being chosen as the second cluster center.
- BThe probability of selecting (2, 2) as the second cluster center is twice that of selecting (0, 2).
- CIf (2, 2) is chosen as the second cluster center, the algorithm will always converge in one iteration.
- DThe tie-breaking rule will affect the final cluster centers.
Read the passage below, then answer Q3–Q4
Based on the above data, answer the given subquestions.
Q3MCQ3 Marks·Choose one optionPerform K-means clustering with K = 2 and initial cluster centers at (−2, 3) and (2,−1). What are the final means of clusters after convergence?
- A
- B
- C
- D
- A
- Q4NAT2 Marks·Write the number
Suppose we introduce a new data point (0, 0) to the existing dataset and perform K means with the same initial cluster centers. How many iterations would it require now for convergence?
Your answer:
End Term · 31 Aug 2025 (FN)End Term
- Q5MSQ2 Marks·Choose all correct options
With respect to the Lloyd’s algorithm, choose the correct statements:
- AThe partition configurations cannot repeat themselves.
- BAfter doing the reassignments (consider at least one point reassigned to the new cluster), we might get the same means for all clusters.
- CObjective function after making the re-assignments strictly reduces.
- DObjective function after making the re-assignments strictly increases.
End Term · 31 Aug 2025 (AN)End Term
- Q6MCQ2 Marks·Choose one option
- AGreedy initialization always achieves F = F_(optimal) by maximizing the distance between initial centers.
- BGreedy initialization may achieve F = F_(optimal) or a larger value depending on the dataset and the initial choice.
- CGreedy initialization does not ensure convergence for K-means clustering.
- DGreedy initialization ensures convergence to a unique solution for any dataset.
End Term · 13 Apr 2025 (FN)End Term
- Q7NAT3 Marks·Write the number
Your answer:
End Term · 13 Apr 2025 (AN)End Term
- Q8MCQ3 Marks·Choose one option
- A
- B
- C
- A
End Term · 22 Dec 2024 (FN)End Term
- Q9MCQ3 Marks·Choose one option
- A3, 5, 1, 4, 2
- B5, 3, 1, 2, 4
- C3, 5, 2, 4, 1
- D5, 3, 1, 4, 2
- ENone of these
End Term · 22 Dec 2024 (AN)End Term
- Q10MSQ3 Marks·Choose all correct options
Select the correct statements from the following for k-means algorithm:
- AIn k-means algorithm, all cluster initializations lead to the same result.
- Bk-means algorithm is sensitive to outliers.
- COne initialization may converge while another may not.
- DThe initialization of cluster centres may affect the number of iterations k- means takes to converge.