NPTEL Introduction to Machine Learning Week 10 Assignment Answers 2025
1. The pairwise distance between 6 points is given below. Which of the option shows the hierarchy of clusters created by single link clustering algorithm?
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2. 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 :-
3. In BIRCH, using number of points N, sum of points SUM and sum of squared points SS, we can determine the centroid and radius of the combination of any two clusters A and B. How do you determine the radius of the combined cluster? (In terms of N,SUM and SS of both two clusters A and B)
Answer :-
4. 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 statement 1.
- Statement 1 is true. Statement 2 is true. Statement 2 is not the correct reason for statement 1.
- Statement 1 is true. Statement 2 is false.
- Both statements are false.
Answer :-
5. Run K-means on the input features of the MNIST dataset using the following initialization:
KMeans(n_clusters=10, random_state=seed)
Usually, for clustering tasks, we are not given labels, but since we do have labels for our dataset, we can use accuracy to determine how good our clusters are.
Label the prediction class for all the points in a cluster as the majority true label. E.g. {a,a,b}
would be labeled as {a,a,a}
What is the accuracy of the resulting labels?
- 0.790
- 0.893
- 0.702
- 0.933
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6.
Answer :-
7. a in rand-index can be viewed as true positives(pair of points belonging to the same cluster) and b as true negatives(pair of points belonging to different clusters). How, then, are rand-index and accuracy from the previous two questions related?
- rand-index = accuracy
- rand-index = 1.18×accuracy
- rand-index = accuracy/2
- None of the above
Answer :-
8. Run BIRCH on the input features of MNIST dataset using Birch(n_clusters=10, threshold=1). What is the rand-index obtained?
- 0.91
- 0.96
- 0.88
- 0.98
Answer :-
9. Run PCA on MNIST 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.
1600, 1522
1500, 1482
1000, 1000
1797, 1742
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