  In recent years, due to the wide applications of uncertain data, mining
frequent itemsets over uncertain databases has attracted much attention. In
uncertain databases, the support of an itemset is a random variable instead of
a fixed occurrence counting of this itemset. Thus, unlike the corresponding
problem in deterministic databases where the frequent itemset has a unique
definition, the frequent itemset under uncertain environments has two different
definitions so far. The first definition, referred as the expected
support-based frequent itemset, employs the expectation of the support of an
itemset to measure whether this itemset is frequent. The second definition,
referred as the probabilistic frequent itemset, uses the probability of the
support of an itemset to measure its frequency. Thus, existing work on mining
frequent itemsets over uncertain databases is divided into two different groups
and no study is conducted to comprehensively compare the two different
definitions. In addition, since no uniform experimental platform exists,
current solutions for the same definition even generate inconsistent results.
In this paper, we firstly aim to clarify the relationship between the two
different definitions. Through extensive experiments, we verify that the two
definitions have a tight connection and can be unified together when the size
of data is large enough. Secondly, we provide baseline implementations of eight
existing representative algorithms and test their performances with uniform
measures fairly. Finally, according to the fair tests over many different
benchmark data sets, we clarify several existing inconsistent conclusions and
discuss some new findings.
