# NPTEL Introduction To Machine Learning – IITKGP Week 8 Assignment Answer 2023

NPTEL Introduction To Machine Learning – IITKGP Week 8 Assignment Solutions

## NPTEL Introduction To Machine Learning – IITKGP Week 8 Assignment Answer 2023

1. What is true about K-Mean Clustering?

1. K-means is extremely sensitive to cluster center initializations
a. 1 and 2
b. 1 and 3
c. All of the above
d. 2 and 3
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2. In which of the following cases will K-Means clustering fail to give good results? (Mark all that apply)
a. Data points with outliers
b. Data points with round shapes
c. Data points with non-convex shapes
d. Data points with different densities

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3. Which of the following clustering algorithms suffers from the problem of convergence at local optima? (Mark all that apply)
a. K- Means clustering algorithm
b. Agglomerative clustering algorithm
c. Expectation-Maximization clustering algorithm
d. Diverse clustering algorithm

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4.

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5. Assume, you want to cluster 7 observations into 3 clusters using K-Means clustering algorithm. After first iteration the clusters: C1, C2, C3 has the following observations:
C1: {1,1), (4,4), (7,7)}
C2: {(0,4), (4,0)}
С3: {(5,5), (9,9)}
What will be the cluster centroids if you want to proceed for second iteration?
a. C1: (4,4), C2: (2,2), C3: (7,7)
b. C1: (2,2), C2: (0,0), C3: (5,5)
c. C1: (6,6), C2: (4,4), C3: (9,9)
d. None of these

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6. Following Question 5, what will be the Manhattan distance for observation (9, 9) from cluster centroid C1 in the second iteration?
a. 10
b. 5
c. 6
d. 7

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7. Which of the following is not a clustering approach?
a. Hierarchical
b. Partitioning
c. Bagging
d. Density-Based

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8. Which one of the following is correct?
b. Single linkage clustering is computationally cheaper compared to K-means clustering.
c. K-Means clustering is computationally cheaper compared to single linkage clustering.
d. None of the above.

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9. Considering single-link and complete-link hierarchical clustering, is it possible for a point to be closer to points in other clusters than to points in its own cluster? If so, in which approach will this tend to be observed?
a. No

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10.

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11. Feature scaling is an important step before applying K-Mean algorithm. What is the reason behind this?
a. In distance calculation it will give the same weights for all features
b. You always get the same clusters if you use or don’t use feature scaling
c. In Manhattan distance it is an important step but in Euclidean it is not
d. None of these

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12. Which of the following options is a measure of internal evaluation of a clustering algorithm?
a. Rand Index
b. Jaccard Index
c. Davis-Bouldin Index
d. F-score

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13. Given, A= {0,1,2,5,6} and B = {0,2,3,4,5,7,9}, calculate Jaccard Index of these two sets.
a. 0.50
b. 0.25
c. 0.33
d. 0.41

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14. Suppose you run K-means clustering algorithm on a given dataset. What are the factors on which the final clusters depend?
I. The value of K
II. The initial cluster seeds chosen
III. The distance function used.
a. only
b. Il only
c. land Il only
d. I, Il and ill

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15. Consider a training dataset with two numerical features namely, height of a person and age of the person. The height varies from 4-8 and age varies from 1-100. We wish to perform K-Means clustering on the dataset. Which of the following options is correct?
a. We should use Feature-scaling for K-Means Algorithm.
b. Feature Scaling can not be used for KMeans Algorithm.
c. You always get the same clusters if you use or don’t use feature scaling.
d. None of these

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