Virtual Labs

1. What is the primary purpose of clustering in data analysis?

a: To group similar data points together based on their characteristics Explanation

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b: To predict future values of data points Explanation

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c: To reduce the dimensionality of the data Explanation

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d: To remove outliers from the dataset Explanation

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Q2. Which algorithm is most suitable for these data points?

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a: DBSCAN Explanation

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b: K-Means Explanation

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c: Both a and b Explanation

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d: None Explanation

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Q3. Let

X_1 = (4,5)

X_2 = (1,4)

X_3 = (0,1)

and

X_4 = (5,0)

, and consider the following 3 partitions:
i)

D_1 = \{X_1, X_2\}

D_2 = \{X_3, X_4\}

ii)

D_1 = \{X_1, X_4\}

D_2 = \{X_2, X_3\}

iii)

D_1 = \{X_1, X_2, X_3\}

D_2 = \{X_4\}

Which partition is favored by the sum-of-squared error? Squared error for each partition is defined by:

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Where

m_i

is the mean of partition

D_i

a: i Explanation

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b: ii Explanation

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c: iii Explanation

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d: None of the above Explanation

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Q4. In MST-based clustering, how is the final clustering result determined?

a: By selecting the threshold that minimizes the ratio of intra-cluster to inter-cluster distances Explanation

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b: By choosing the threshold that creates the maximum number of clusters Explanation

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c: By using a fixed threshold value for all datasets Explanation

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d: By selecting the threshold that maximizes the number of edges in the MST Explanation

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Q5. What is the main advantage of using MST-based clustering over K-means?

a: It can automatically determine the optimal number of clusters Explanation

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b: It requires less computational resources Explanation

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c: It always produces spherical clusters Explanation

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d: It doesn't require any distance calculations Explanation

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Q6. What is the role of the minimum spanning tree in MST-based clustering?

a: It connects all data points with minimum total edge length while preserving the natural structure of the data Explanation

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b: It creates a hierarchical structure of the data points Explanation

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c: It reduces the dimensionality of the data Explanation

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d: It identifies outliers in the dataset Explanation

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Data Clustering: K-means, MST based