Ivy_Honeywell_Data Anlaytics_6.2

Which of the following is the most appropriate strategy for data cleaning before performing clustering analysis, given less than desirable number of data points:

(i) Capping and flooring of variables

(ii) Removal of outliers

• (I) only

• (ii) only

• (I) and (ii)

• None of the above

What is the minimum no of variables/features required to perform clustering?

• 0

• 1

• 2

• 3

For two runs of k-mean clustering is it expected to get same clustering results?

• YES

• NO

Is it possible that assignment to clusters does not change between successive iterations in K-means?

• YES

• NO

• Can't Say

• None of these

What could be the possible reason(s) for producing two different dendograms using agglomerative clustering algorithm for the same dataset?

• Proximity function used

• Of data points used

• Of variables used

• b. and c. only

• All of the above

In which of the following cases will k-means clustering fail to give good results?

(i) Data points with outliers

(ii) Data points with different densities

(iii) Data points with round shape

(iv) Data point with non-convex shapes

• (i) and (ii)

• (ii) and (iii)

• (ii) and (iv)

• (i) , (ii) and (iv)

• (i), (ii) , (iii) and (iv)

Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering?

(i) Single Link

(ii) Complete Link

(iii) Average Link

• (i) and (ii)

• (i) and (iii)

• (ii) and (iii)

• (i), (ii) and (iii)

Which of the following is/are true?

(i) Clustering analysis is negatively affected by multicollinearity of features

(ii) Clustering analysis is negatively affected by heteroscedasticity

• (i) only

• (ii) only

• (i) and (ii)

• None of the above

Which of the following is/are valid iterative strategy for treating missing values before clustering analysis?

• Imputation with mean

• Nearest neighbour assignment

• Imputation with Expectation Maximization algorithm

• All of the above

Assume you want to cluster 7 observations into 3 clusters using K-means clustering algorithm. After first iteration clusters, C1, C2, C3 has the following observations:

C1: {(2, 2), (4, 4), (6, 6)}

C2: {(0, 4), (4, 0)}

C3: {(5, 5), (9, 9)}

What will be the cluster centroids if you want to proceed for second iteration?

• C1: (4, 4), C2: (2, 2), C3: (7, 7)

• C1: (6, 6), C2: (4, 4), C3: (9, 9)

• C1: (2, 2), C2: (0, 0), C3: (5, 5)

• None of the above