PROCEEDINGS IPMU '08
A New Algorithm for Mining Frequent Itemsets from Evidential Databases
Mohamed Anis Bach Tobji, Boutheina Ben Yaghlane, Khaled Mellouli.
Association rule mining (ARM)
problem has been extensively tackled
in the context of perfect data.
However, real applications showed
that data are often imperfect (incomplete
and/or uncertain) which
leads to the need of ARM algorithms
that process imperfect databases. In
this paper we propose a new algorithm
for mining frequent itemsets
from evidential databases. We introduce
a new structure called RidLists
that is the vertical representation of
the evidential database. Our structure
is adapted to itemsets belief
computation which makes the mining
algorithm more efficient. Experimental
results showed that our
proposed algorithm is efficient in
comparison with the only evidential
ARM algorithm in the literature
[10].
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