All MCQs

Every maximal frequent itemset is closed, but not every closed itemset is maximal.
Closed sets retain more support information.

Maximal itemsets mark the boundary of frequency.
They provide stronger compression but do not preserve supports of all subsets.

Closed itemsets compress frequent-pattern output without losing support information.
Any itemset sharing support with a larger superset is not closed.

Confidence uses the antecedent support in its denominator.
Therefore confidence(X → Y) can differ from confidence(Y → X).

Lift depends on the joint support divided by the product of marginal supports.
Reversing X and Y leaves this value unchanged.

Conviction focuses on rule violations where X occurs without Y.
Higher values indicate fewer violations than expected under independence.

Leverage is support(XY) minus support(X)support(Y).
Zero leverage corresponds to independence.

The consequent occurs less often with the antecedent than expected from its baseline.
Such a rule describes avoidance or negative dependence.

A lift of one means confidence equals the baseline support of the consequent.
Knowing X provides no association-based advantage for predicting Y.

Lift above one means Y is more common when X occurs than in the dataset overall.
The rule reflects positive dependence.

Lift compares the observed conditional occurrence of Y with its overall frequency.
It therefore evaluates whether X and Y co-occur more or less than expected under independence.

If Y occurs in nearly every transaction, many antecedents will appear to predict it.
Lift compares confidence with Y's baseline support.