Data Science MCQ for QA

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A cluster can be expressed as

• Group of similar objects that differ significantly from other objects
• To transform or simplify data in order to prepare it for a machine-learning algorithm
• Symbolic representation of facts or ideas from which information can potentially be extracted
• None of the above

Which of the following is finally produced by Hierarchical Clustering?

• Mutually exclusive clusters of arbitrary shape
• Dendrogram
• Mean Squared Error
• d) All of the above mentioned

Which of the following clustering technique requires the merging approach?

• Partitioning
• Hierarchical- Agglomerative
• Hierarchical- Divisive
• Naive Bayes

Which of the following is required to start K-means clustering?

• Distance measure
• Number of clusters
• Initial cluster centres
• All of the mentioned

A good clustering method will produce high-quality clusters

• high intra-class similarity and low inter-class similarity
• low intra-class similarity and low inter-class similarity
• low intra-class similarity and high inter-class similarity
• None of the above

Calculate the Euclidean distance between a = (3, 8) and b = (3, 3)

• 2.236
• 25
• 5
• None of the above

The three cluster outputs of the K-Means algorithm after the first iteration are shown below (C1, C2, C3). C1: {(2,3), (4,5)} C2: {(1,5), (5,1)} C3: {(6,6), (8,8)} Calculate the cluster centroid before proceeding for the second iteration.

• C1: (3, 4), C2: (3, 3), C3: (7,7)
• C1: (4, 5), C2: (5,1), C3: (8,8)
• C1: (2,3), C2: (1,5), C3: (6,6)
• None of these

What is a Dendrogram?

• A tree representation used to illustrate the arrangement of clusters in hierarchical clustering.
• A tree representation to illustrate the arrangement of clusters in partitional clustering.
• A type of hierarchical clustering.
• A type of bar chart diagram to visualize k-means clusters.

What are the two types of Hierarchical Clustering?

• Top-Down Clustering
• Bottom-Top Clustering
• Dendrogram
• K-means Clustering