  The Apriori algorithm is a classical algorithm for the frequent itemset
mining problem. A significant bottleneck in Apriori is the number of I/O
operation involved, and the number of candidates it generates. We investigate
the role of LSH techniques to overcome these problems, without adding much
computational overhead. We propose randomized variations of Apriori that are
based on asymmetric LSH defined over Hamming distance and Jaccard similarity.
