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NeuroCOLT
Technical Report NC-TR-01-092
2001-092
Finding
Frequent Itemsets With At Most One Negated Attribute
I Fortes, JL Balcazar,
R Morales
ABSTRACT
In Data Mining applications of the frequent sets problem, such
as finding association rules, a commonly used generalization is to
see each transaction as the characteristic function of the corresponding
itemset. This allows one to find also correlations between items not
being in the transactions; but this may lead to the risk of a large
and hard to interpret output. We consider the problem where facts
consisting of items not being in the transactions are desired to be
taken into account, but only in limited form; specifically, we present
an algorithm to construct all frequent itemsets consisting of an arbitrary
number of present positive attributes and at most one present negative
attribute. The algorithm takes advantage of the relationship between
the corresponding frequencies of such items.
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