# MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support
## 1 Introduction  
Big data analytics applications have drastically changed our daily life. The gradually maturing sensor technology and ubiquitous use of various mobile devices generate increasing amounts of data, which is expected to continue increasing by 35% annually [1]. Interest in data-driven decision making is growing in response to escalating data generation, and a large variety of big data analytics applications have been emerging accordingly. Developing models to find critical information and analyzing value from big data have become the subject of deep exploration and intense discussion [2; 3; 4].  
Big data analytics and applications are interesting and important prominent topics [5]. Identifying frequent itemsets in large transactional databases, known as frequent patterns (FP), and the FP of association rules can be valuable to decision makers for setting up strategies [6; 7]. Among those commonly-applied FP-mining algorithms, the Apriori algorithm is regarded as a classic method [8; 9]. The Apriori algorithm uses a bottom-up approach to generate candidate itemsets. As the quantity of data is extremely large, the bottom-up approach becoming lower processing efficiency. To overcome the limitations of the traditional Apriori algorithm, [PERSON] et al. proposed an FP-growth algorithm that included two phases: constructing an FP-tree and mining the FP-tree that would be moreefficient [10; 11]. For both Apriori and FP-growth algorithms, it is important to set reasonable minimum support values [12; 13].  
However, obviously all items in the database vary widely from many perspectives such as their price or profit. Generally, if only one minimum support is used for FP mining, those high-profit items mostly with lower selling frequencies, such as refrigerators and smartphones, would not be considered as frequent patterns [14]. Furthermore, lower minimum support would lead to the generation of many meaningless association rules, which would complicate the decision-making process [15]. Therefore, using a single minimum support for all items in the database is insufficient for FP analysis. Single item support cannot comprehensively address the complexity of items in large datasets. Thus, it is important and necessary to consider multiple item supports while big data analysis is applied in practice. Recently, several studies have proposed the concept of multiple item supports (MIS) to address the trade-offs required by decision makers [16; 17]. For FP mining research, applying MIS to FP mining warrants further analysis. In addition, a large amount of research has attempted to improve the algorithmic efficiency of FP mining when working with big data. Apache's Hadoop system applies MapReduce programming to solve these problems encountered in processing big data [18]. Using Hadoop MapReduce to implement the parallelization of traditional association rule mining approaches, such as the Apriori and FP-growth algorithms, was propose to improve the overall performance of frequency pattern mining [19]. For illustration, the k-phase parallel Apriori algorithm was proposed to identify k-frequency items in k-scans using Hadoop MapReduce [20].  
To optimize FP mining, this study proposes a multiple item support frequent pattern (MISFP)-growth algorithm, which mines association rules from FP by using multiple item support. Furthermore, to improve the efficiency of the analysis, the proposed algorithm is deployed on Hadoop MapReduce architecture. Section 2 summarizes and discusses the related researches. Section 3 details the proposed algorithm of Hadoop-based MISFP-growth algorithm, and Section 4 demonstrates the proposed MISFP-growth algorithm with an example implementation. Finally, three experiments are implemented to validate the accuracy and performance of the proposed Hadoop-based MISFP-growth algorithm. According to the experiment results, the proposed algorithm achieved enhanced performance that is viable for practical applications in big data analytics.