Fast approximation of probabilistic frequent closed itemsets

In recent years, the concept of and algorithm for mining probabilistic frequent itemsets (PFIs) in uncertain databases, based on possible worlds semantics and a dynamic programming approach for frequency calculations, has been proposed. The frequentness of a given itemset in this scheme can be characterized by the Poisson binomial distribution. Further and more recently, others have extended those concepts to mine for probabilistic frequent closed itemsets (PFCIs), in an attempt to reduce the number and redundancy of output. In addition, work has been done to accelerate the computation of PFIs through approximation, to mine approximate probabilistic frequent itemsets (A-PFIs), based on the fact that the Poisson distribution can closely approximate the Poisson binomial distribution---especially when the size of the database is large. In this paper, we introduce the concept of and an algorithm for mining approximate probabilistic frequent closed itemsets (A-PFCIs). A new mining algorithm for mining such concepts is introduced and called A-PFCIM. It is shown through an experimental evaluation that mining for A-PFCIs can be orders of magnitude faster than mining for traditional PFCIs.