# Incremental Algorithm for Association Rule Mining under Dynamic Threshold
## 4 The Proposed Algorithm
## 7 Conclusions

This study has addressed the ARM incremental problem by enhancing the popular Apriori algorithm [2; 13]. The ARM algorithms extract knowledge from the database, where the extracted knowledge reflects the database when no change is introduced. Indeed, the database status in real-time applications is changeable, and the database can be manipulated by numerous database operations (add, update, and delete). Many algorithms have been proposed to overcome this issue in the ARM incremental field. Nevertheless, several weaknesses and defects exist and remain, such as the following: the present approaches target, in the mining, the newly extended database only for those transactions that were added after the last mining, and they suppose, with the passage of time, that the state of the old database is constant and that no change has been made; neither deleted nor updated transactions. Based on Table 1, many cases demand rescanning all databases, particularly when there is a frequent itemset in the new database, but infrequent in the old database. In this case, scanning the database demands checking if the new itemsets are frequent over the entire database. Moreover, thresholds are one of the most popular problems in most learning techniques. In ARM, knowledge discovery relies on specific thresholds; so any changes in threshold indicate that the discovered knowledge does not reflect the database anymore. The proposed algorithm allows change in the thresholds and retrieves new knowledge based on the new thresholds without rescanning the database. The proposed algorithm perfectly managed to overcome the previous outstanding issues, apart from providing effective and viable solutions for a range of domains. The IApriori algorithm was tested in many experiments by placing focus on two specific features: execution time, and the similarity of extracted knowledge between the proposed algorithm and the others, as results showed the improvement made by the IApriori on Tables 17-19 and Figures 1-5. This was performed to ascertain the accuracy attained in extracting knowledge. Methodology, software, and orginal draft preparation, I.A. Validation and supervision N.A.G. Editing C.M. Editing and review J.M. Review and testing N.S.S. This research was funded by University Malaya through a postgraduate research grant (PPP) grant number PG106-2015B. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Add** & **Update** & **Delete** & **Rescan Solved** & **Support Accuracy** & **Threshold Changes** \\ \hline FUP [33] & ✓ & X & X & X & X & X \\ NFUP [34] & ✓ & X & X & X & X & X \\ IMSC [35] & ✓ & X & X & X & X & ✓ \\ MAAP [36] & ✓ & X & X & X & X & X \\ IApriori & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Comparison between the proposed method and incremental algorithms. ## References

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