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Choose an option to check your answer.
A.
A model that assigns observations to predefined classes
B.
A method that only groups unlabeled data
C.
A database indexing structure
D.
A rule that finds frequent itemsets
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Correct Answer: A. A model that assigns observations to predefined classes
Explanation:
A classifier learns a decision rule from labeled examples.
It then predicts class labels for new observations.
Choose an option to check your answer.
A.
The confidence of a rule ending at the node
B.
The number of classes
C.
The distance from the root
D.
The number of transactions sharing that prefix up to the node
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Correct Answer: D. The number of transactions sharing that prefix up to the node
Explanation:
Counts aggregate repeated transaction prefixes.
A node count can differ from counts of its descendants because fewer transactions continue farther.
Choose an option to check your answer.
A.
It changes the meaning of support
B.
It ignores long itemsets
C.
It reduces database scans and avoids explicit candidate counting
D.
It assumes every item is independent
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Correct Answer: C. It reduces database scans and avoids explicit candidate counting
Explanation:
Apriori repeatedly generates and counts candidate levels.
FP-growth reuses the compressed tree for recursive mining.
Choose an option to check your answer.
A.
It stores no counts
B.
The FP-tree and conditional trees may not fit comfortably in memory
C.
It requires one model per transaction
D.
It can process only one item
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Correct Answer: B. The FP-tree and conditional trees may not fit comfortably in memory
Explanation:
Large, weakly compressible datasets can produce substantial tree structures.
Disk-based or distributed variants may be required.
Choose an option to check your answer.
A.
No, it still uses minimum support to define frequent items and patterns
B.
Yes, all patterns are always returned
C.
Yes, because counts are unnecessary
D.
No, because it uses class labels instead
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Correct Answer: A. No, it still uses minimum support to define frequent items and patterns
Explanation:
Minimum support remains the criterion for pattern frequency.
The algorithm changes the search strategy, not the definition.
Choose an option to check your answer.
A.
One-nearest neighbor
B.
Naive Bayes
C.
Linear regression
D.
FP-growth
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Correct Answer: D. FP-growth
Explanation:
FP-growth avoids generating all candidate combinations explicitly.
Its compressed tree is often more efficient for dense transactional data.
Choose an option to check your answer.
A.
It changes item supports
B.
It makes all rules causal
C.
Consistent ordering enables maximum prefix sharing
D.
It creates target labels
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Correct Answer: C. Consistent ordering enables maximum prefix sharing
Explanation:
Transactions with the same frequent items align on common initial paths.
Inconsistent ordering would fragment the representation.
Choose an option to check your answer.
A.
It represents the most frequent item
B.
It is a null starting node for all transaction paths
C.
It stores the class label
D.
It contains the minimum support value only
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Correct Answer: B. It is a null starting node for all transaction paths
Explanation:
All filtered transaction paths begin under the root.
The root itself does not correspond to an item.
Choose an option to check your answer.
A.
It preserves the support information needed for frequent itemsets
B.
It stores every raw attribute value unchanged
C.
It preserves transaction order in time
D.
It stores all infrequent items
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Correct Answer: A. It preserves the support information needed for frequent itemsets
Explanation:
Although transactions are compressed, shared-prefix counts and node links retain relevant frequency information.
The original transaction list need not be reproduced.
Choose an option to check your answer.
A.
No frequent pattern exists
B.
Only one-itemsets are valid
C.
The data require classification
D.
All combinations of items along the path can form patterns with the suffix
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Correct Answer: D. All combinations of items along the path can form patterns with the suffix
Explanation:
A single path represents nested item occurrence.
Combinations can be generated directly using minimum node counts.
Choose an option to check your answer.
A.
When all transactions are identical
B.
When there is only one frequent item
C.
When transactions share few prefixes
D.
When support is exactly one
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Correct Answer: C. When transactions share few prefixes
Explanation:
Limited prefix overlap reduces path sharing.
The tree may then approach the size of the filtered transaction data.
Choose an option to check your answer.
A.
When every transaction contains completely unique items
B.
When many transactions share common item prefixes
C.
When no item is frequent
D.
When items are continuous measurements
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Correct Answer: B. When many transactions share common item prefixes
Explanation:
Shared prefixes are stored once with aggregated counts.
Dense overlap therefore produces a compact tree.