# Performance Evaluation of Sequential Rule Mining Algorithms
## 5 Conclusions  
This paper presents a meaningful comparison of three algorithms designed for the task of sequential rule mining, which is especially common to several sequences. Each algorithm was analyzed utilizing four real datasets with different features. For example, one dataset had long sequential patterns with low diversity of items that forced us to use a low minSup value to generate the sequential rules. Another dataset had short sequential patterns with a high diversity of items. Another one was a huge dataset that caused an overhead limit excess, which made us resort to using a subset of the original dataset. Our experiments indicated that the performance of the algorithms is associated with the features of the datasets. Moreover, experience shows that it can reduce the execution time and control the number of valid rules generated by several orders of size restrictions such as adjusting the window size constraint or determine the number of generated rules or change the value of the minSup threshold. With a specified value of a window size constraint, mining the TRuleGrowth algorithm can run faster and the correctness of discovered sequential rules that are not constrained by the arrangement can be increased. The NRD-DBV algorithm could be used to reduce the number of sequential rules and memory requirements. It relies on a DBV structure with a prefix-tree that leads to early pruning of child nodes to reduce the search space. In addition, the NRD-DBV method generates more rules for taking item arrangement into account than the TRuleGrowth algorithm. The researchers conclude that each algorithm has its own use in the fields of application of sequential rule mining to achieve the highest possible efficiency. NRD-DBV algorithm has many applications in error detection, intervention, and bugs. It is useful in domains that require the arrangement of items such as in medical area (for example, if the patient is suffering from a fever, which is followed by a decrease in the level of coagulation, followed by the appearance of red marks on the body, it is reasonable that the patient will need to be treated for dengue fever. This order in events is important in predicting an appropriate type of treatment). Additionally, it can be used in marketing to design the most personalized strategy, and also in software engineering where ordering is mostly important to accomplish its tasks. The TRuleGrowth algorithm implementation allows the allocation of optional parameters like maximizing the number of items that appear in the antecedent and consequent of a rule. It can be useful in making product recommendations and performing fast decision making. For future work, we intend to improve the NRD-DBV algorithm by using another concise representation, such as maximal or generator patterns, to improve performance in the large sequences database. Figure 27: Comparison of memory usage for the TNS with the NRD-DBV algorithm for (BMS webview2) dataset. **Author Contributions:** Conceptualization, A.A. and N.Y.; methodology, A.A.; software, N.Y.; validation, A.A. and N.Y.; formal analysis, N.Y.; investigation, A.A.; resources, A.A.; data curation, N.Y.; writing--original draft preparation, N.Y.; writing--review and editing, A.A.; visualization, N.Y.; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported through the Annual Funding track by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. AN000519]. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.