MCQ Collection
Data Mining MCQs
Practice Data Mining questions with answers and explanations.
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Correct Answer: D. Distances become less discriminative as dimensions increase
Explanation:
High-dimensional space is sparse and nearest points may still be far away.
Irrelevant features further obscure meaningful neighborhoods.
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Correct Answer: D. Computing inner products in an implicit higher-dimensional feature space
Explanation:
A kernel enables nonlinear boundaries without explicitly constructing all transformed features.
The SVM optimization uses pairwise kernel values.
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Correct Answer: A. Each centroid is recalculated as the mean of its assigned points
Explanation:
Updated means minimize squared error within each fixed cluster assignment.
Assignment and update repeat until convergence.
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Correct Answer: D. Their distances are sensitive to measurement units
Explanation:
A high-range variable can dominate pairwise distances.
Standardization gives variables more comparable influence.
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Correct Answer: D. Identifying rare or structurally unusual observations without anomaly labels
Explanation:
Unsupervised methods assume anomalies differ from dominant data structure.
Rare does not always mean harmful, so results require investigation.
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Correct Answer: D. The number of direct connections a node has
Explanation:
In an undirected network, degree is the count of adjacent edges.
In directed networks, in-degree and out-degree are distinguished.
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Correct Answer: A. Low, because it mainly stores the training data
Explanation:
KNN has little parameter fitting.
Its main computational burden occurs during prediction.
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Correct Answer: A. The linear kernel
Explanation:
The linear kernel uses the ordinary dot product.
It is effective for many high-dimensional sparse problems.
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Correct Answer: B. K-means can converge to different local optima
Explanation:
The objective is not jointly convex over assignments and centers.
Multiple starts improve the chance of finding a better solution.
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Correct Answer: A. Cut the tree at a selected height
Explanation:
A horizontal cut separates the dendrogram into disconnected branches.
Each branch below the cut becomes a cluster.
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Correct Answer: A. Mostly or exclusively normal examples
Explanation:
The model learns the normal region and flags departures.
One-class SVM is a common example.
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Correct Answer: A. Nodes that frequently lie on shortest paths between other nodes
Explanation:
High-betweenness nodes can act as brokers or bridges between groups.
Their removal may disrupt communication across the network.
