“…Mining frequent itemsets from transactional databases play an important role in many data mining applications, e.g., social network mining ( Jiang, Leung, & Zhang, 2016;Moosavi, Jalali, Misaghian, Shamshirband, & Anisi, 2017 ), finding gene expression patterns ( Becquet, Blachon, Jeudy, Boulicaut, & Gandrillon, 2001;Creighton & Hanash, 2003;Cremaschi et al, 2015;Mallik, Mukhopadhyay, & Maulik, 2015 ), web log pattern mining ( Diwakar Tripathia & Edlaa, 2017;Han, Cheng, Xin, & Yan, 2007;Iváncsy, Renáta, & Vajk, 2006;Yu & Korkmaz, 2015 ). In recent years, many algorithms have been proposed for efficient mining of frequent itemsets ( Apiletti et al, 2017;Bodon, 2003;Burdick, Calimlim, Flannick, Gehrke, & Yiu, 2005;Gan, Lin, Fournier-Viger, Chao, & Zhan, 2017;Han, Pei, & Yin, 20 0 0;Kosters & Pijls, 2003;Liu, Lu, Yu, Wang, & Xiao, 2003;Pei, Tung, & Han, 2001;Uno, Kiyomi, & Arimura, 2004;Vo, Pham, Le, & Deng, 2017 ). These algorithms take a transactional database and support threshold (minimum itemset support) as input and mines complete set of frequent itemsets with support greater than minimum itemset support .…”