# Incremental Algorithm for Association Rule Mining under Dynamic Threshold
## 4 The Proposed Algorithm
The requirement in the example is \(Minsupp=50\%\) (three transactions). Based on that threshold value, the frequent itemset is listed in Table 15, while the final generated association rule for incremental example is presented in Table 16. The next section briefly describes the dataset. ## 5 Dataset

The set of benchmark datasets were mined in numerous experiments to assess the proposed algorithm. The first was the Apriori dataset, which was evaluated using the proposed algorithm, wherein the ability to derive the same output in a specific threshold by the original Apriori algorithm was tested. It contained 75K records for supermarket database transactions with the dataset having 49 items. The dataset served as the benchmark dataset as it was produced by the Integrated and Project Management [37]. The second dataset refers to chess, whereby the dataset was obtained from the sports discipline with 3196 games and 36 attributes. Since the values of the attributes were categorized and the records had class attributes, they were suitable for classification mining. These were retrieved from fini [38]. The third dataset refers to mushrooms, which contained imageries of hypothetical samples that matched 23 mushroom species designed specifically from mushroom hunters. Each item was labelled as edible, toxic, unknown, and unusable. The latter classification was combined with that of toxic. The dataset had 23 attributes and 8124 cases [38]. The dataset is available online at [38]. The fourth dataset refers to T1014D100K dataset, which can be retrieved at [38]. The dataset is a market basket that contains 100K transactions with 1000 items. The fifth dataset is composed of breast cancer dataset, which was generated and published by the University Medical Center, Institute of Oncology. The breast cancer dataset contained 286 instances of medical cases. The dataset schema had nine attributes, which were split into linear and nominal attributes. The nominal attribute was categorized using the discretization method. The dataset is available online at [39]. Finally, the last dataset refers to the acute inflammations dataset, which resulted from the collaboration between the Laboratory of Intelligent Systems on Polish Academy of Sciences and medical expertise. The purpose of this dataset is to test the expert systems in performing a presumptive medical diagnosis for two diseases of the urinary system. This dataset had eight attributes, and 120 instances with each instance reflecting a patient's case. The dataset is available at [39]. ## 6 Results and Discussion

This section presents the empirical outputs derived from IApriori algorithm, along with comparison of the results with those obtained from baseline algorithm and AprioriTID. The experiments were performed to test and compare the scalability of the IApriori algorithms. The experimental work evaluated the changes noted in the threshold for the chess dataset. Table 17 and Figure 1 present the retrieved results. The knowledge from the output appeared similar for both algorithms. The IApriori displayed exceptional achievements in light of execution time, whereby \begin{table}
\begin{tabular}{l l l} \hline \hline
**List Index** & **Rule** & **Confidence** \\ \hline
1 & \(a\Rightarrow b\) & 75\% \\
2 & \(b\Rightarrow a\) & 75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Generated rule set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**List Index** & **Itemset** & **Support** & \(<tid>\) \\ \hline
1 & \(\{a\}\) & 4 & \(\{t_{1},t_{4},t_{5},t_{6}\}\) \\
2 & \(\{b\}\) & 4 & \(\{t_{1},t_{2},t_{5},t_{6}\}\) \\
3 & \(\{c\}\) & 3 & \(\{t_{2},t_{4},t_{6}\}\) \\
4 & \(\{ab\}\) & 3 & \(\{t_{1},t_{5},t_{6}\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Frequent itemset \(L_{f}\). IApriori algorithm had successfully decreased the processing time between 25% and 50%. Meanwhile, the Apriori algorithm took between two and four times longer processing time to perform the same tasks.