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Feasible itemset distributions in data mining: theory and application

Computing frequent itemsets and maximally frequent item-sets in a database are classic problems in data mining. The resource requirements of all extant algorithms for both problems depend on the distribution of frequent patterns, a topic that has not been formally investigated. In this paper, we study properties of length distributions of frequent and maximal frequent itemset collections and provide novel solutions for computing tight lower bounds for feasible distributions. We show how these bounding distributions can help in generating realistic synthetic datasets, which can be used for algorithm benchmarking.