  Traditional association rule mining based on the support-confidence framework
provides the objective measure of the rules that are of interest to users.
However, it does not reflect the utility of the rules. To extract non-redundant
association rules in support-confidence framework frequent closed itemsets and
their generators play an important role. To extract non-redundant association
rules among high utility itemsets, high utility closed itemsets (HUCI) and
their generators should be extracted in order to apply traditional
support-confidence framework. However, no efficient method exists at present
for mining HUCIs with their generators. This paper addresses this issue. A
post-processing algorithm, called the HUCI-Miner, is proposed to mine HUCIs
with their generators. The proposed algorithm is implemented using both
synthetic and real datasets.
