## 4 C-Monoa
### Main Steps of C-Monoa
The average support for the generated rules is recorded at 0.24, while the average confidence stands at 0.42, the average Kulc value is 0.21, and the average comprehensibility is 0.39, further highlighting its robust performance in association rule mining. Lastly, Figure 4c showcases the results for the Wholesale Customers dataset. Here too, C-MONOA maintains a competitive edge over other algorithms in the majority of scoring functions, achieving average scores of 0.47 for support, 0.50 for confidence, 0.27 for the Kulc value, and 0.65 for comprehensibility. From the results, it can be seen that the rule scores mined by C-MONOA are generally better than those mined by MONOA, demonstrating the superior chaos traversal ability of the cubic chaotic map. To reduce the impact of method randomness, all algorithms are executed 10 times on each dataset to ensure the reliability and effectiveness of the results. And the results were statistically evaluated through average support \((Sup_{mean})\), average confidence \((Conf_{mean})\), average Kulc \((Kulc_{mean})\), and average comprehensibility \((Comp_{mean})\). Table 7 is the average results of multiple runs of C-MONOA and multi-objective evolutionary algorithms on three datasets. From the table, it can be seen that C-MONOA ranks first in \(Sup_{mean}\), \(Conf_{mean}\), and \(Comp_{mean}\) on the three datasets, indicating its excellent adaptability in complex tasks. Although \(Kulc_{mean}\) is slightly inferior, the overall effect is still very stable, reflecting its strong advantages and robustness. In addition, the P-value between C-MONOA and each algorithm was tested using the Wilcoxon rank sum test, with a significance level of 0.05. Among them, \"+\", \"-\", and \"\(\approx\)\" indicate that the algorithm is significantly better than C-MONOA, significantly worse than C-MONOA, and statistically similar to C-MONOA. As shown in Table 8, C-MONOA is significantly better than other algorithms in terms of

Figure 4: Comparison of scores of different algorithms on different datasets. (**a**) Cardiovascular Disease dataset. (**b**) Heart Disease dataset. (**c**) Wholesale Customers dataset. support, confidence, and comprehensibility in the vast majority of cases. In terms of Kulc, it is similar in significance to other algorithms in most cases, with only a few cases being significantly weaker than other algorithms. Experiments conducted on three distinct datasets: Cardiovascular Disease, Heart Disease, and Wholesale Customers, demonstrate the effectiveness of the C-MONOA. The findings indicate that employing the Michigan method for chromosome encoding is adept at managing mixed datasets comprising both continuous and discrete data types. Additionally, the integration of the cubic chaotic map during the initialization of the population enhances the algorithm's ability to thoroughly explore the search space in its early stages, thereby mitigating the risk of prematurely converging on local optima. The results further demonstrate that the C-MONOA surpasses traditional association rule mining techniques and achieves outstanding performance across multiple key evaluation metrics. This underscores the algorithm's robust ability to effectively handle complex datasets, providing high-quality and reliable outcomes. Its adaptability in managing mixed data types, versatility across various domains, and consistent capability to generate interpretable and actionable association rules highlight its practical value and robustness. While the experiments do not directly evaluate profitability, the extracted high-quality insights have significant potential to inform decision-making in fields such as healthcare and retail, leading to efficiency gains and economic benefits. Collectively, these characteristics establish the C-MONOA as a versatile and impactful tool in association rule mining tasks.