Introduction to Machine Learning Week 10 NPTEL Assignment Answers 2025

NPTEL Introduction to Machine Learning Week 10 Assignment Answers 2024

1. K-means algorithm is not a particularly sophisticated approach for Image Segmentation tasks. Choose the best possible explanation from below which supports the claim:

• It takes no account of the spatial proximity of different pixels.
• The curse of dimensionality does not affect the performance of K-means algorithm, as it effectively handles high-dimensional data with minimal loss of accuracy.
• The algorithm requires the number of clusters (K) to be specified beforehand.
• Initialization does not affect K

• means.

Answer :- a, c

2. The pairwise distance between 6 points is given below. Which of the option shows the hierarchy of clusters created by single link clustering algorithm?

Answer :- b

3. For the pairwise distance matrix given in the previous question, which of the following shows the hierarchy of clusters created by the complete link clustering algorithm.

Answer :- b

5. Statement 1: CURE is robust to outliers.
Statement 2: Because of multiplicative shrinkage, the effect of outliers is dampened.

• Statement 1 is true. Statement 2 is true. Statement 2 is the correct reason for statemnet 1.
• Statement 1 is true. Statement 2 is true. Statement 2 is not the correct reason for statemnet 1.
• Statement 1 is true. Statement 2 is false.
• Both statements are false.

Answer :- a

6. Which of the following statements about the Rand Index is true?

• It is insensitive to the permutations of cluster labels
• It is biased towards larger clusters
• It cannot handle overlapping clusters
• It is unaffected by outliers in the data

Answer :- a

8. Run BIRCH on the input features of iris dataset using Birch(n_clusters=5, threshold=2). What is the rand-index obtained?

• 0.68
• 0.71
• 0.88
• 0.98

Answer :- b

9. Run PCA on Iris dataset input features with n components = 2. Now run DBSCAN using DBSCAN(eps=0.5, min samples=5) on both the original features and the PCA features. What are their respective number of outliers/noisy points detected by DBSCAN?
As an extra, you can plot the PCA features on a 2D plot using matplotlib.pyplot.scatter with parameter c = y pred (where y pred is the cluster prediction) to visualise the clusters and outliers.

• 10, 10
• 17, 7
• 21, 11
• 5, 10

Answer :- b