MCQ Collection
Data Mining MCQs
Practice Data Mining questions with answers and explanations.
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Correct Answer: C. It reduces the chance of a tied vote
Explanation:
An odd number of neighbors cannot split evenly between two classes.
Ties can still arise through weighted voting or distance equality.
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Correct Answer: C. The distance between the separating hyperplane and the nearest class points
Explanation:
The margin measures separation around the decision boundary.
SVM optimization attempts to maximize it while controlling violations.
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Correct Answer: C. Using high-confidence model predictions as temporary labels for unlabeled data
Explanation:
A supervised model first predicts unlabeled examples.
Selected predictions are added to training as if they were labels.
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Correct Answer: A. With all observations in one cluster
Explanation:
Divisive clustering recursively splits broad groups into smaller clusters.
It builds the hierarchy from the top downward.
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Correct Answer: C. The low-dimensional layout can distort some high-dimensional relationships
Explanation:
No low-dimensional projection preserves every distance and neighborhood perfectly.
Interpretation should be supported by quantitative diagnostics.
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Correct Answer: C. Extracting useful information from the content of web pages
Explanation:
Web content includes text, images, metadata, and structured page elements.
Methods such as text mining and information extraction are commonly used.
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Correct Answer: D. Use nonempty proper subsets as antecedents and the remaining items as consequents
Explanation:
Each split of L into disjoint nonempty parts defines a candidate rule.
Confidence filtering then retains sufficiently strong directions.
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Correct Answer: D. An FP-tree built from the conditional pattern base of a suffix item
Explanation:
It represents frequent prefixes associated with a chosen suffix.
Recursive mining extends those prefixes into larger frequent patterns.
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Correct Answer: D. Choosing models and hyperparameters before final testing
Explanation:
Validation data guide development decisions without touching the test set.
The final evaluation is then performed once on held-out test data.
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Correct Answer: D. Random folds can expose future information to earlier training periods
Explanation:
Forecast evaluation should mimic predicting the future from the past.
Random mixing creates leakage and unrealistic performance.
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Correct Answer: D. By testing thresholds such as x ≤ t
Explanation:
Candidate thresholds divide ordered numeric values into regions.
The algorithm selects a threshold that improves class purity.
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Correct Answer: D. Majority classes receive larger prior probabilities unless adjusted
Explanation:
Empirical priors reflect observed class frequencies.
Alternative priors can be specified when training prevalence is not appropriate.
