## 3 Materials and Methodology
### Association Rule  
Association rule (AR) is an important part of machine learning with the purpose of revealing the interrelationships hidden in massive amount of data [35]. Association rule technique is more adaptable, because there is no requirement for whether the sample data obey a normal distribution or satisfy a correlation test using association rule technique [34]. The concept of association rule is as follows: assume that \(I=\{i_{1},i_{2},\ldots,i_{n}\}\) is a set of items (items), the length of an itemset denotes the number of items contained in an itemset, and an itemset of length k is called a k-item set. Given a transaction database \(T=\{t_{1},t_{2},\ldots,t_{n}\}\), where each t is a non-empty subset of I. The association rules are denoted as \(X\to Y\), where \(X\subset I,Y\subset I,X\cap Y=\varnothing\). \(X\) is the left-hand-side (LHS) and \(Y\) is the right-hand-side (RHS). Each association rule is identified by support, confidence and lift [35], which are calculated as follows [38]:  
\[Support(X\to Y)=P(X\cup Y) \tag{1}\]  
\[Confidence(X\to Y)=P(Y/X) \tag{2}\]  
\[Lift(X\to Y)=conf.(X\to Y)/supp.(Y) \tag{3}\]  
Support of \(X\to Y\) represents the probability that the data set contains both \(X\) and \(Y\)[39]. Confidence is used to measure the probability of \(X\) occurring if \(Y\) occurs, i.e., the conditional probability. A rule is considered strongly associated if it is greater than or equal to the minimum support and minimum confidence [40]. Lift considers the probability of \(Y\) changing when \(X\) occurs to characterize the degree of association between the LHS and RHS, which could avoid the interference of pseudo-strong association rules and prevent invalid association rules appearing in the final result. A rule is regarded as a valid strong association rule when the lift value is greater than 1 [41]. These three thresholds are manually set according to the needs of data mining [42; 43].  
There are three typical association rule mining algorithms: Apriori, FP-Growth, and Eclat. However, no matter which algorithm is used, the basic association rule mining process is the same, which consists of two parts: mining frequent itemsets and extracting association rules [32]. First, all frequent itemsets are mined from the database, which are the itemsets with support greater than or equal to the minimum support. The k-itemsets that satisfy this condition are called frequent k-itemsets. Then, association rules are generated, which are the frequent itemsets with confidence greater than or equal to the minimum confidence.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Category** & **Variable** & **Frequency** & **Category** & **Variable** & **Frequency** \\ \hline \multirow{8}{*}{Environment factors} & Lack of safety awareness (P10) & 13.66\% & & Failure to follow the rules in & \\  & Lack of situational & & & & \\  & awareness (P11) & 5.46\% & & & \\  & Distraction (P12) & 5.88\% & & & \\  & Fatigue (P13) & 7.35\% & & & \\  & Over-confidence (P14) & 0.84\% & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._Eclat is a classical algorithm for searching frequent itemsets in vertical data formats [38]. The core idea of the Eclat algorithm is to divide the search space into smaller subspaces using prefix-based equivalence relations, and each subspace is searched independently using a bottom-up search method to generate frequent item sets. The flowchart is illustrated in Figure 2.