Biblio

Over the past two decades, itemset mining techniques have become an integral part of pattern mining in large databases. We present a novel system for mining frequent and rare itemsets simultaneously with supports weighted by cardinality in transactional datasets. Based on our neural item embedding with additive compositionality, the original mining problems are approximately reduced to polynomial-time convex optimization, namely a series of vector subset selection problems in Euclidean space. The numbers of transactions and items are no longer exponential factors of the time complexity under such reduction, except only the Euclidean space dimension, which can be assigned arbitrarily for a trade-off between mining speed and result quality. The efficacy of our method reveals that additive compositionality can be represented by linear translation in the itemset vector space, which resembles the linguistic regularities in word embedding by similar neural modeling. Experiments show that our learned embedding can bring pattern itemsets with higher accuracy than sampling-based lossy mining techniques in most cases, and the scalability of our mining approach triumphs over several state-of-the-art distributed mining algorithms.