MCQ Collection
Data Mining MCQs
Practice Data Mining questions with answers and explanations.
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Correct Answer: D. Gives closer neighbors greater influence than farther neighbors
Explanation:
Distance weighting recognizes that nearby examples may be more relevant.
A common rule uses inverse distance weights.
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Correct Answer: D. An SVM that permits no training classification violations
Explanation:
Hard-margin separation requires perfectly linearly separable training data.
It is highly sensitive to noise and outliers.
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Correct Answer: D. Early prediction errors can reinforce themselves during retraining
Explanation:
Incorrect pseudo-labels become training targets and may amplify bias.
Confidence thresholds and iterative monitoring reduce the risk.
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Correct Answer: C. The smallest distance between any cross-cluster pair
Explanation:
Single linkage merges clusters based on their nearest members.
It can recover elongated shapes but is prone to chaining.
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Correct Answer: D. A single observation that is unusual relative to the rest of the data
Explanation:
Point anomalies are individually abnormal without needing additional context.
An extreme fraudulent transaction is a common example.
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Correct Answer: D. Analyzing hyperlinks and the graph structure of the web
Explanation:
Pages are nodes and hyperlinks are directed edges.
Link analysis can identify authority, importance, and communities.
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Correct Answer: A. Euclidean distance
Explanation:
Euclidean distance measures straight-line separation in feature space.
Standardization is important because it is scale-sensitive.
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Correct Answer: A. An SVM that allows some margin violations or misclassifications
Explanation:
Slack variables permit imperfect separation.
The model balances a wide margin against training errors.
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Correct Answer: A. The sum of squared distances from points to their assigned centroids
Explanation:
K-means seeks compact clusters around arithmetic means.
Its standard objective is within-cluster sum of squared errors.
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Correct Answer: D. Clusters join through sequences of close points despite weak overall compactness
Explanation:
A narrow bridge of points can connect otherwise distinct groups.
This may create long, loosely connected clusters.
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Correct Answer: A. An observation abnormal only under a particular context
Explanation:
Context may include time, location, season, or user group.
A high temperature may be normal in summer but anomalous in winter.
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Correct Answer: A. Discovering behavior patterns from logs of user interactions
Explanation:
Clickstreams, sessions, and server logs reveal navigation behavior.
Applications include personalization and website optimization.
