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?**

- K-means is extremely sensitive to cluster center initializations
- Bad initialization can lead to Poor convergence speed
- Bad initialization can lead to bad overall clustering

a. 1 and 2

b. 1 and 3

c. All of the above

d. 2 and 3

Answer :- For AnswerClick Here

**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

Answer :-For AnswerClick Here

**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

Answer :-For AnswerClick Here

**4. **

Answer :-For AnswerClick Here

**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

Answer :-For AnswerClick Here

**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

Answer :-For AnswerClick Here

**7. Which of the following is not a clustering approach?**a. Hierarchical

b. Partitioning

c. Bagging

d. Density-Based

Answer :-For AnswerClick Here

**8. Which one of the following is correct?**a. Complete linkage clustering is computationally cheaper compared to single linkage.

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.

Answer :-For AnswerClick Here

**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

b. Yes, single-link clustering

c. Yes, complete-link clustering

d. Yes, both single-link and complete-link clustering,

Answer :-For AnswerClick Here

**10. **

Answer :-For AnswerClick Here

**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

Answer :-For AnswerClick Here

**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

Answer :-For AnswerClick Here

**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

Answer :-For AnswerClick Here

**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

Answer :-For AnswerClick Here

**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 K–Means Algorithm.

c. You always get the same clusters if you use or don’t use feature scaling.

d. None of these

Answer :-For AnswerClick Here

Course Name | Introduction To Machine Learning – IITKGP |

Category | NPTEL Assignment Answer |

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