Frequent itemset mining is a data mining technique to discover the frequent patterns from a collection of databases. However, it becomes a computational expensive task when it is used for mining large volume of data. Hence, there is a necessity for a scalable algorithm that can handle bigger datasets. Binary-based Technique Algorithm (BBT) can simplify the process of generating frequent patterns by using bit wise operations and binary database representation. However, it still suffers with the problem of low performance when dealing with high volume of data and a minimum values of support threshold to generate the list of frequent itemset patterns. This is due to its design which run in a single thread of execution. This research proposed a Parallel Binary-Based Algorithm (P-BBA) to solve the mentioned problem. The proposed algorithm is designed with collaborative threads which simultaneously work together to generate frequent itemsets in a big data environment. A master/slave architecture is used to fit the algorithm with distributed computing platform. The obtained results showed significant reductions in execution time when using the proposed parallel binary-based algorithm. © 2021 Little Lion Scientific