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Top1. Introduction
One important problem in data mining is the discovery of frequent itemsets in a transactional database (Leung, Chan, & Chung, 2006). Frequent itemset mining is a traditional and important problem in data mining. An itemset is frequent if its support is not less than a minimum support specified by users. Traditional frequent itemset mining approaches have mainly considered the problem of mining static transaction databases. In these methods, transactions are stored in secondary storage so that multiple scans over the data can be performed. Frequent patterns, such as frequent itemsets, substructures, sequences term-sets, phrase sets, and sub graphs, generally exist in real-world databases. Identifying frequent itemsets is one of the most important issues faced by the knowledge discovery and data mining community. Frequent itemset mining plays an important role in several data mining fields as association rules (Chandak, Girase, & Mukhopadhyay, 2015; Mousavi et al., 2017; (Bimonte et al., 2017; Hamidi et al., 2016), warehousing (Daraei et al., 2016; Hamidi, 2011, 2012, 2009, 2010, 2011, 2017), correlations, clustering of high-dimensional biological data, and classification (Lin et al., 2002; Hashemzadeh et al., 2016; Mohammadi et al., 2005).