# Research and Application of an Improved Sparrow Search Algorithm
## 1 Introduction  
Association rule mining (ARM) was originally utilized to suggest a solution to the shopping basket issue and has grown to be one of the major fields of data mining [1]. Association rules are implicit formulae, such as \(X\to Y\), where \(X\) and \(Y\) are considered the predecessor and successor of association rules, and \(X\) and \(Y\) represent separate sets of entries in the database. ARM assists decision makers in numerous businesses to make proper judgments to increase their competitiveness by finding possible dependencies between items in the transactional database and is presently employed in various domains, such as traffic accidents [2], education [3], and medicine [4]. The Apriori method is the most generally used association rule mining technique [5]. However, it regularly searches the whole database and creates a huge number of candidate itemsets, which leads to a considerable amount of I/O overheads. The FP-Growth minimizes the number of scans of the database by compressing the data into the shape of a tree but creates a high number of unordered tree nodes, which causes the complexity of the data structure to rise [6]. The optimization of classic association rule mining algorithms is near the bottleneck, the mining efficiency is too low in the face of larger-dimensional data, and it has to preset the support and confidence criteria in advance, so it is less applicable in diverse circumstances. In recent years, the major strategy to overcome this challenge has been to propose metaheuristic algorithms [7; 8]. This family of algorithms has been extensively employed in numerous study fields and is a typical way of tackling combinatorial and hyperparametric optimization issues [9]. Using this class of algorithms to handle association rule mining has more reasonable time and resource overheads. However, when facing multiple high-dimensional datasets, it is also a new challenge to obtain more comprehensive association rules in a complex solution space [10]. Metaheuristic algorithms are frequently motivated by various natural occurrences and human social behaviors [11], such as genetic algorithms [12], slime mold algorithms [13],ant lion algorithms [14], etc. Such algorithms are one of the efficient techniques for tackling the association rule mining issue [8]. In [15; 16], the particle swarm and genetic algorithm were used for association rule mining; this strategy does not need one to provide a minimum support or confidence criterion, which improves the applicability of the scenario. [PERSON] et al. 's use of fuzzy logic and the whale optimization algorithm for association rule mining in [17] reduced the number of scans of the database and the memory required for computation. The use of metaheuristic algorithms effectively improves the mining efficiency of association rule problems and saves resource overheads. Researchers have started focusing on enhancing the quality of mining rules based on the optimization goals of association rules. These objectives may be categorized into single-objective and multi-objective algorithms [18]. Typical single-objective algorithms are GAR [19] and GENAR [20], but in order to pursue the accuracy and diversity of the rules in more scenarios, the association rules are a multi-objective problem. For example, in [21] weighted combinations of multiple performance indicators developed fitness functions that fulfilled the demands of the decision makers, but for the setting of the weights it was difficult to determine the appropriate value, and it was difficult to equalize between different indica tors. In [22], the Pareto optimal method was used to optimize confidence, comprehensibility, and interest simultaneously. This method does not need to set the objective weights; it will obtain a series of Pareto optimal solutions. The set of these solutions makes up the Pareto frontiers that show solutions with trade-offs under multiple objectives, providing a range of choices for the decision maker [23].