## 4 C-Monoa
### Main Steps of C-Monoa
Through experimental verification across diverse datasets, including those pertaining to cardiovascular disease, heart disease, and wholesale customer behavior, C-MONOA has

\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & & **MOPSO** & **MOBMSASA** & **MOWOA** & **MOFA** & **MONOA** \\ \hline Cardiovascular & & & & & & \\ Disease & & & & & & \\ \hline Support & \(p\)-value & 0.0001827 & 0.0001776 & 0.0001816 & 0.0001786 & 0.5205 \\  & \(+/-/\approx\) & \(-\) & \(-\) & \(-\) & \(-\) & \(\approx\) \\ Confidence & \(p\)-value & 0.002202 & 0.01709 & 0.0001813 & 0.0001826 & 0.0003298 \\  & \(+/-/\approx\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Kulc & \(p\)-value & 0.01906 & 0.004044 & 0.3846 & 0.4273 & 0.002193 \\  & \(+/-/\approx\) & \(\approx\) & \(+\) & \(\approx\) & \(\approx\) & \(-\) \\ Comprehensibility & \(p\)-value & 0.0001786 & 0.0002461 & 0.0001821 & 0.0001825 & 0.4051 \\  & \(+/-/\approx\) & \(-\) & \(-\) & \(-\) & \(-\) & \(\approx\) \\ \hline Heart Disease & & & & & & \\ \hline Support & \(p\)-value & 0.0001826 & 0.0001774 & 0.0001817 & 0.0001827 & 0.0002817 \\  & \(+/-/\approx\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Confidence & \(p\)-value & 0.1212 & 0.0001813 & 0.0001826 & 0.02569 & 0.001699 \\  & \(+/-/\approx\) & \(\approx\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Kulc & \(p\)-value & 0.7336 & 0.5704 & 0.0003281 & 0.0001817 & 0.001486 \\  & \(+/-/\approx\) & \(\approx\) & \(\approx\) & \(-\) & \(+\) & \(-\) \\ Comprehensibility & \(p\)-value & 0.0001816 & 0.0001806 & 0.0001816 & 0.0001806 & 0.5964 \\  & \(+/-/\approx\) & \(-\) & \(-\) & \(-\) & \(-\) & \(\approx\) \\ \hline Wholesale & & & & & & \\ Customers & & & & & & \\ \hline Support & \(p\)-value & 0.0001796 & 0.0001796 & 0.0001806 & 0.0001816 & 0.0001806 \\  & \(+/-/\approx\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Confidence & \(p\)-value & 0.0001806 & 0.0001826 & 0.00058 & 0.02831 & 0.0002836 \\exhibited outstanding rule quality and discovery capabilities. Specifically, these datasets varied in composition, reflecting real-world scenarios with different characteristics and complexities. Despite these variations, C-MONOA has consistently demonstrated significant superiority over traditional algorithms such as MOPSO, MOBMASAA, MOWOA, MOFA, and MONOA in the majority of cases. Overall, C-MONOA has proven its effectiveness and robustness in mining association rules for mixed continuous and discrete data, adapting well to the unique conditions and attributes of each dataset. Although the C-MONOA has achieved good preliminary results in numerical association rule mining, it still faces challenges in large-scale datasets, application domains, attribute interval optimization, and other aspects. Future research will focus on the scalability of algorithms, the diversity of application scenarios, and more intelligent interval selection mechanisms. Through further exploration of these issues, C-MONOA is expected to play an important role in multiple fields and become an effective tool for handling complex data mining problems. [PERSON] designed the study, performed the research, analyzed data, and wrote the paper; [PERSON] polished the article; [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript. This work was supported by the Department of Science and Technology of Guizhou Province, grant number QKH [2022] 242; and the National Natural Science Foundation of China, grant number 61561009. Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no competing interests. ## References

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