# Research and Application of an Improved Sparrow Search Algorithm
## 2 Related Work  
Traditional association rule mining algorithm: The earliest algorithm to solve the association rule mining problem was Apriori [5]. In the AprioriTid algorithm [24], proposed later, the TID set is used in the process of each calculation to replace the frequent itemset, reducing the number of scans of the transaction database and thus reducing the overheads on computer memory. Subsequently, based on the Apriori algorithm, many new improved versions have been proposed, such as the DHP [25] and DIC [26] algorithms, but it is still difficult to change the nature of the original Apriori because it will be scanned many times on the transaction database, resulting in a large amount of resource overheads. [PERSON] et al. proposed the FP-Growth algorithm [6]. Compared with the Apriori algorithm, it no longer needs to generate candidate frequent itemsets and uses a tree structure for storing compressed dataset information, called FP-tree, only needing to scan the transaction database twice, thus greatly reducing the demand for computing resources. Aiming at the traditional association rule mining algorithm optimization has been a bottleneck; it is difficult to improve on the basis of the original algorithm. Research scholars have tried to introduce heuristic algorithms into the association rule mining problem.  
Association rule mining based on heuristic algorithms: The use of genetic algorithms for association rule mining [19], compared with the traditional algorithms, greatly enhances mining efficiency. However, this method only starts from a single-objective perspective and lacks comprehensive consideration. [PERSON] et al. [27] combined different intelligent optimization algorithms based on the whale optimization algorithm into a hybrid whale algorithm and linearly weighted the support, confidence, and accuracy factors into a new adaptive function in order to pursue a new optimization function that is based on the whale optimization algorithm. A new fitness function in pursuit of better rule quality: Linear weighting of different objective functions is a simple and effective way, but it is difficult to determine the appropriate value for the setting of weights, which requires a certain amount of a priori knowledge of the decision maker. Different datasets have different emphasis, which leads to the difficulty of striking a balance between the different indicators, and the weights set by human beings have a certain degree of subjectivity. So, [PERSON] et al. [22] used the Pareto optimal method to measure multi-objective association rules. [PERSON] proposed a MOB-ARM algorithm [28] that uses four quality measures: support, confidence, comprehensibility, and interest, aiming to mine more useful and understandable rules. [PERSON] used a multi-objective version of a genetic algorithm for association rule mining [29], targeting support, comprehensibility, and interest, all three. On this basis, we propose an improved sparrow search algorithm for multi-objective association rule mining, aiming to enhance the heuristic algorithm's ability to find the best and improve the value of the objective function obtained.