## 1 Introduction  
Today, electronic communication users generate various kinds of information at increasing rates. Data mining is a data analysis approach for identifying information of interest that is contained in data. Data mining is successfully applied in various fields, including statistics, artificial intelligence, expert systems, visualization, and machine learning. Data mining mainly includes frequent pattern mining [1], sequential mining [2], classification, and clustering. This method can discover hidden information from a dataset and can also be applied to various fields like market basket analysis, discovery of high-utility patterns [3] and so forth. A typical frequent pattern mining problem is discovering the itemsets with support greater than or equal to a specific threshold in a database. An itemset is a combination of different items, and the support is a value reflecting how frequently the itemset appears in the database. The support of \(X\) with respect to database \(D\) is defined as the proportion of a number of transactions in the database containing the itemset \(X\). Frequent pattern mining is the core method of data mining,which can determine that an itemset satisfies the minimum support threshold. The support threshold also influences the outcome of frequent patterns. The algorithm for discovering frequent patterns is separated into two parts: an Apriori-like approach and a frequent-pattern (FP) tree. Apriori is the first algorithm proposed by [PERSON] et al. [1] for mining association rules. First, the user-defined minimum support threshold is used to find itemsets, and then frequent patterns from these itemsets are discovered. This algorithm generates many candidate itemsets when constructing association rules; thus, it needs to scan the database several times. The execution efficiency is very low. To improve the execution time, an algorithm called FP-growth has been previously proposed, which is an extended prefix-tree approach. This algorithm first scans the database and then records a list of items in descending order of frequency. Further, it scans the database again, obtains the conditional pattern bases from the FP tree, and constructs a conditional FP tree from the conditional pattern bases. Using the FP-tree structure just only scans the database twice, which reduces the input/output time and improves the execution efficiency. With the increasing amount of data being produced, the size of databases must also increase. Big data is a term describing datasets that are too large to handle in conventional ways. As [PERSON], the world-famous statistician, said, \"when data gets big, big problems can arise.\" This means that a number of problems are associated with big data, and one of the most pressing problems is executive performance. Several research approaches use distributed or parallel systems such as grid computing [4; 5] or other computing techniques to handle big data, which we review in this study. To accelerate the mining process and improve execution performance, numerous studies have attempted to develop algorithms that operate in distributed computing environments. Fast mining is the core for big data applications [6; 7]. [PERSON] et al. [8] proposed five dispatch methods based on the cloud-based association rule mining (CARM) [9] algorithm: (a) equal working set (EWS), (b) request on demand (ROD), (c) small size working set (SSWS), (d) progressive size working set (PSWS), and (e) FLR-mining [8] algorithms. EWS uses an equal allocation of computing nodes; however, the completed time of each computing node is unknown, which results in unbalanced loading. The ROD algorithm is proposed to deal with the unbalanced loading of EWS. This method involves a kernel node to assign new tasks after a computing node completes a task. Therefore, the ROD algorithm can address the problem of unbalanced loading; however, the algorithm spends much time in communication. SSWS and PSWS are proposed to solve the drawbacks of the abovementioned methods.