# Incremental Algorithm for Association Rule Mining under Dynamic Threshold
## 4 The Proposed Algorithm
The results clearly exhibit the potential of IApriori algorithm in terms of speed, when compared to other algorithms. The time values portrayed in the table are in milliseconds. The next experiment involved the mushroom dataset with under different values of support threshold, whereby outcomes retrieved from the two algorithms were compared. The IApriori algorithm displayed improvement for execution time from 15% to 80%. Table 18 and Figure 2, which present the empirical findings for the mushroom dataset, show that the improvement rates were rather high. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Support** & **Apriori** & **AprioriTID** & **IApriori** & **Improvement (\%)** \\ \hline
0 & 699 & 702 & 705 & 0.64 \\
1 & 675 & 502 & 170 & 246.18 \\
2 & 635 & 328 & 49 & 882.65 \\
3 & 575 & 389 & 49 & 883.67 \\
4 & 585 & 309 & 66 & 577.27 \\
5 & 575 & 339 & 58 & 687.93 \\
6 & 525 & 419 & 75 & 529.33 \\
7 & 485 & 389 & 80 & 446.25 \\
8 & 475 & 339 & 59 & 589.83 \\
9 & 465 & 319 & 90 & 335.56 \\
10 & 462 & 309 & 100 & 285.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: The result of experiments over the chess dataset (executing time). Figure 1: Experimental results for chess dataset (execution time). The \(T10|4D100K\) dataset was used to test the IApriori algorithm, whereby the results were compared with those obtained from AprioriTID and Apriori. Figure 3 and Table 19 tabulate the experimental results involving \(T10|4D100K\) dataset. The results of both IApriori and AprioriTID algorithms appeared logical and reasonable, while the outcomes retrieved from Apriori took longer time. It is emphasized here that the experimental setting was retained similar for all algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Support** & **Apriori** & **AprioriTID** & **IApriori** & **Improvement (\%)** \\ \hline
0 & 632 & 801 & 518 & 38.32 \\
1 & 709 & 526 & 70 & 782.14 \\
2 & 655 & 617 & 58 & 996.55 \\
3 & 488 & 591 & 57 & 846.49 \\
4 & 578 & 466 & 59 & 784.75 \\
5 & 539 & 488 & 61 & 741.80 \\
6 & 426 & 434 & 69 & 523.19 \\
7 & 253 & 362 & 72 & 327.08 \\
8 & 306 & 399 & 65 & 442.31 \\
9 & 415 & 395 & 70 & 478.57 \\
10 & 351 & 264 & 69 & 345.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: The result of experiments over mushroom dataset (executing time). Figure 2: Experimental results for mushroom dataset (execution time). The following table presents the numerical outputs from experiments using the T10 1 4D100K dataset. Figure 4 clearly presents the recorded findings. Figure 4 portrays the comparison of results obtained from IApriori algorithm with those of other tested algorithms on the breast cancer dataset. Apparently, IApriori achieved the best execution time. For the first experiment (when threshold was zero), the IApriori demanded additional time, but was still less when compared to the other Apriori algorithms. The following experiments required no time less than 100 ms. For instance, when the support threshold was 4, IApriori recorded 95 ms, whereas 350 and 550 ms for AprioriTID and Apriori, respectively. This achievement reflects outstanding improvement up to 300%. \begin{table}
\begin{tabular}{c c c c c} \hline
**Support** & **Apriori** & **AprioriTID** & **IApriori** & **Improvement (\%)** \\ \hline
0 & 20,000,000 & 386 & 509 & 1,964,574 \\
1 & 19,604,746 & 408 & 87 & 11,267,230 \\
2 & 19,205,401 & 386 & 151 & 6,359,432 \\
3 & 18,785,613 & 374 & 92 & 10,209,676 \\
4 & 18,423,390 & 404 & 107 & 8,609,150 \\
5 & 18,031,557 & 460 & 111 & 8,122,430 \\
6 & 17,654,125 & 456 & 70 & 12,610,315 \\
7 & 17,240,452 & 427 & 87 & 9,908,451 \\
8 & 16,901,257 & 438 & 102 & 8,285,045 \\
9 & 16,571,963 & 458 & 100 & 8,286,111 \\
10 & 16,212,692 & 396 & 84 & 9,650,548 \\ \hline \end{tabular}
\end{table} 
Table 19: The result of experiments over T10 1 4D100K Dataset (Executing Time). Figure 3: Experimental results for T10 1 4D100K dataset (execution time). Figure 5 shows the experimental results obtained from the algorithms for Acute Inflammations dataset.