6. INCREMENTAL PROBABILISTIC FRE-
of the AIQ to k − m, where m is the number of highest fre-
quentness probability items already returned. Any itemsets
QUENT ITEMSET MINING (I-PFIM)
that “fall off” the end can safely be ignored. The rational
Our probabilistic frequent itemset mining approach allows
behind this approach is that for an itemset X at position p
the user to control the confidence of the results using τ .
in the AIQ, p − 1 itemsets with a higher frequentness than
However, since the number of results depends on τ , it may
X exist in the AIQ by construction. Additionally, any of
prove difficult for a user to correctly specify this parameter
the m itemsets that have already been returned must have
without additional domain knowledge. Therefore, this Sec-
a higher frequentness probability. Consequently, our top-k
tion shows how to efficiently solve the following problems,
algorithm contrains the size of the initial AIQ to k and re-
which do not require the specification of τ;
duces its size by one each time a result is reported. The
algorithm terminates once the size of the AIQ reaches zero.
• Top-k probabilistic frequent itemsets query: return the
k itemsets that have the highest frequentness proba-
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