NPTEL Introduction To Machine Learning – IITKGP Week 8 Assignment Answers 2024
1. Do the clustering results of the K-Means algorithm depend on the initial cluster centroid choices?
A) Yes
B) No
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2. Which of the following can act as possible termination conditions in K-Means?
I. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.
Il. Centroids do not change between successive iterations.
A) I only
B) II only
C) I and II
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3. 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)}
C3: ((5,5), (9,9)}
What will be the cluster centroids after the first 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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4. In single-link clustering, the similarity of two clusters is the similarity of their most similar members. What is the time complexity of the single-link clustering algorithm? (Note: n is the number of data points)
A) O(n?)
B) 0(n? log n)
c) 0(n’ log n)
D) 0(n3)
Answer :-
5.
Answer :-
6. Is it possible that assignment of observations to clusters does not change between successive iterations of K-means?
A) Yes
B) No
C) Can’t say
D) None of these
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7. Which of the following is not a clustering approach?
A) Hierarchical
B) Partitioning
C) Bagging
D) Density-Based
Answer :-
8. In which of the following cases will K-Means clustering fail to give good results?
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 :-
9. Given, A = (0,1,2,5,6} and B = (0,2,3,4,5,7,9), calculate Jaccard Index of these tio sets.
A) 0.50
B) 0.25
C) 0.33
D) 0.41
Answer :-
10. Which of the following statements is/are not true about k-means clustering?
A) It is an unsupervised learning algorithm
B) Overlapping of clusters is allowed in k-means clustering
C) It is a hard-clustering technique
D) k is a hyperparameter in k-means
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