# NPTEL Learning Analytics Tools Week 7 Assignment Answers 2024

By Sanket

## NPTEL Learning Analytics Tools Week 7 Assignment Answers 2024

1. What is the primary goal of K-means clustering?

• To maximize the distance between different clusters
• To assign each point to its nearest cluster center
• To minimize the distance within each cluster
• To increase the number of clusters until all points are isolated
`Answer :- For Answers Click Here `

2. Which of the following is a limitation of K-means clustering?

• It is insensitive to the initial placement of cluster centers
• It always finds the global optimum solution
• It can only handle numerical data
• It requires the number of clusters to be specified in advance
`Answer :- For Answers Click Here `

3. In K-means clustering, how are the new cluster centers determined after assigning points to clusters?

• By randomly selecting new points as cluster centers
• By calculating the median of all points in each cluster
• By calculating the mean of all points in each cluster
• By selecting the farthest point from the current cluster center
`Answer :- `

4. Given the following data points: (2, 3), (3, 3), (6, 5), (8, 8), and (9, 10), if the initial cluster centers are (2, 3) and (8, 8), which of the following are the correct cluster assignments after the first iteration?

• Cluster 1: (2, 3), (3, 3), (6, 5); Cluster 2: (8, 8), (9, 10)
• Cluster 1: (2, 3), (3, 3); Cluster 2: (6, 5), (8, 8), (9, 10)
• Cluster 1: (2, 3), (3, 3), (9, 10); Cluster 2: (6, 5), (8, 8)
• Cluster 1: (2, 3), (6, 5); Cluster 2: (3, 3), (8, 8), (9, 10)
`Answer :- For Answers Click Here `

5. You have the following data points: (1, 1), (2, 2), (4, 4), and (5, 5). If the initial cluster centers are (1, 1) and (5, 5), what will the new cluster centers be after the first iteration?

• (1.5, 1.5) and (4.5, 4.5)
• (2, 2) and (5, 5)
• (1, 1) and (4, 4)
• (1, 1) and (5.5, 5.5)
`Answer :- `

6. You have 6 data points in a 2-dimensional space: (1, 2), (2, 1), (4, 5), (5, 4), (8, 9), and (9, 8). You are using K-means clustering with k=3. The initial cluster centers are (1, 2), (4, 5), and (9, 8). After the first iteration, what is the total within-cluster sum of squares (WCSS)?

• 20.5
• 17
• 15.5
• 18
`Answer :- `

7. Which of the following statements best describes hierarchical clustering?

• It divides the dataset into a predetermined number of clusters
• It creates a nested sequence of clusters through a series of merges or splits
• It assigns data points to the nearest cluster center iteratively
• It requires the number of clusters to be specified in advance.
`Answer :- For Answers Click Here `

8. In hierarchical clustering, what is the difference between agglomerative and divisive approaches?

• Agglomerative starts with one cluster and splits it, while divisive starts with individual points and merges them
• Agglomerative is a bottom-up approach, and divisive is a top-down approach
• Agglomerative uses centroids, while divisive uses medoids
• Agglomerative clustering requires the number of clusters in advance, while divisive does not
`Answer :- `

9. Given the following data points: (1, 2), (2, 3), (5, 6), (8, 8). Using single-linkage (nearest neighbor) agglomerative hierarchical clustering, what is the distance between the first two clusters that will be merged?

• 1.0
• 1.41
• 5.0
• 3.0
`Answer :- `

10. You are given five data points: (1, 1), (2, 1), (4, 3), (5, 4), and (9, 8). After performing agglomerative hierarchical clustering using average linkage, the first two points to be merged are (1, 1) and (2, 1). What is the new cluster center?

• (1.5, 1)
• (1, 2)
• (2, 2)
• (1, 1.5)
`Answer :- For Answers Click Here `