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NPTEL Introduction to Machine Learning Assignment Answers Week 8
Q1. For two runs of K-Mean clustering is it expected to get same clustering results?
Answer: b. No
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Q2. Which of the following can act as possible termination conditions in K-Means?
I.For a fixed number of iterations.
II. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.
III. Centroids do not change between successive iterations.
IV. Terminate when RSS falls below a threshold
A. I, III and IV
B. I, II and III
C. I, II and IV
D. All of the above
Answer: D. All of the above
Q3. After performing K-Means Clustering analysis on a dataset, you observed the following dendrogram. Which of the following conclusion can be drawn from the dendrogram?
a.There were 28 data points in clustering analysis.
b. The best no. of clusters for the analysed data points is 4.
c. The proximity function used is Average-link clustering.
d. The above dendrogram interpretation is not possible for K-Means clustering analysis.
Answer: d. The above dendrogram interpretation is not possible for K-Means clustering analysis.
Q4. What should be the best choice of no. of clusters based on the following results:
Answer: c. 3
Q5. Given, six points with the following attributes:
|point||x coordinate||y coordinate|
Which of the following clustering representations and dendrogram depicts the use of MIN or Single link proximity function in hierarchical clustering:
Answer: Option A
Q6. Is it possible that assignment of observations to clusters does not change between successiveiterations of K-means?
c. Can’t say
d. None of these
Answer: a. Yes
Q7. What is the possible reason(s) for producing two different dendograms using agglomerative clustering for the same data set?
a. Proximity function
b. No. of data points
c. Variables used
d. All of these
Answer: d. All of these
Q8. Which of the following algorithms suffer from the problem of convergence at local optima?
I. K-means clustering
II. Agglomerative clustering
III. Expectation-minimization clustering
IV. Divisive clustering
a. I and II
b. II and III
c. III and IV
d. I and III
Answer: d. I and III
Q9. Which of the following is/are valid iterative strategy before performing clustering analysis for treating missing values?
a. Imputation with mean
b. Nearest neighbour assignment
c. Imputation with expectation-maximization algorithm
d. None of these
Answer: c. Imputation with expectation-maximization algorithm
Q10. If two variables V1 and V2 are used for clustering, which of the following is/are true with K means clustering algorithm for K=3?
I. If V1 and V2 have a correlation of 1, cluster centroid will be in a straight line.
II. If V1 and V2 have a correlation of 0, cluster centroid will be in a straight line.
a. I only
b. II only
c. I and II
d. None of these
Answer: a. I only
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