## 2 Related Works
### Methods Based on Discretization  
[PERSON] and [PERSON] presented a method for mining quantitative association rules by pre-discretizing numeric attributes into a fixed number of intervals [22]. However, this method discretizes attributes in isolation, without considering the correlations with other attributes. [PERSON] and [PERSON] introduced the Relative Unsupervised Discretization (RUDE) algorithm, which combines aspects of both unsupervised and supervised discretization methods [23]. RUDE projects the structure of source attributes onto the target attribute to create split points that reflect the interdependencies between attributes, demonstrating effectiveness and robustness in the presence of noise and inaccuracies.  
[PERSON] et al. (2003) proposed a widely applicable general model for mining fuzzy association rules, focusing on how to define association rules in fuzzy transactions and evaluate their support and confidence [24]. This model is based on fuzzy set theory and extends the concepts of \"itemsets\" and \"transactions\" in traditional association rule mining to fuzzy transactions and fuzzy itemsets, solving many problems of data ambiguity and uncertainty in practical applications.  
[PERSON] et al. (2011) proposed a high-dimensional classification method based on fuzzy association rules, aimed at addressing the challenges of large datasets and high-dimensional problems. There are three steps in the process: fuzzy association rule extraction, candidate rule pre-screening, and genetic algorithm rule selection and parameter tuning [25]. This method uses a genetic algorithm to synergistically optimize rule selection and membership function tuning, which not only improves the accuracy of the rule set but also effectively reduces the number of rules and computational complexity in high-dimensional problems. This model is particularly suitable for large-scale datasets, demonstrating the efficiency of fuzzy rules in classification problems, especially on real datasets.  
In the context of granular computing, [PERSON] et al. conducted a cross-comparison of equal width and equal frequency methods for discretizing attributes in granular rule mining [26]. Their research demonstrated that discretization serves as an effective preprocessing strategy for discovering more robust rules. Additionally, they found that the equal frequency method produces a greater number of rules compared to the equal width approach. The choice of interval settings plays a critical role in these discretization techniques, with optimal configurations significantly improving the rule-mining process.  
[PERSON] et al. (2013) introduced two fuzzy association rule mining models that combine fuzzy C-means clustering with the Apriori algorithm to enhance prediction accuracy [27]. The first model (FCM-Apriori) converts continuous datasets into fuzzy data through the fuzzy C-means algorithm, then applies Apriori to extract fuzzy association rules and combines it with the fuzzy inference system (FIS) for prediction. The second model (FCM-MSapriori) solves the limitation of a single minimum support by introducing a multi-support (MIS) mechanism, which better handles rare item sets and enhances the model's ability to predict rare item sets.  
[PERSON] proposed using two-step clustering for data discretization, which automatically selects the optimal number of clusters using the Bayesian Information Criterion (BIC) [28]. This method takes into account the distinct data distribution characteristics of each attribute, resulting in the generation of more effective rules compared to traditional discretization techniques.