All MCQs

The algorithm mines from shorter suffix-based conditional structures.
Each recursive step expands valid frequent itemsets.

It represents frequent prefixes associated with a chosen suffix.
Recursive mining extends those prefixes into larger frequent patterns.

Prefix paths summarize contexts in which the suffix item appears.
Their counts support construction of a conditional FP-tree.

Header links allow efficient traversal of an item's occurrences across the tree.
They are used to construct conditional pattern bases.

The count records how many transactions share the represented prefix.
Links connect nodes to parents, children, and same-item occurrences.

A common global order allows transactions to share prefixes.
Descending frequency generally maximizes compression.

Infrequent items cannot belong to a frequent itemset.
Removing them reduces the tree and preserves relevant pattern information.

The first scan finds frequent items and their order.
The second inserts filtered, ordered transactions into the tree.

FP-growth compresses transactions into an FP-tree and mines the structure recursively.
This avoids Apriori's candidate explosion.

Lower support finds more and rarer patterns but increases workload and output volume.
Higher support is efficient but may overlook important associations.

Lk is joined to form candidates Ck+1.
Infrequent candidates do not contribute because their supersets cannot be frequent.

This is the candidate prune step based on anti-monotonicity.
It prevents counting candidates that cannot possibly be frequent.