# Incremental Algorithm for Association Rule Mining under Dynamic Threshold
## 4 The Proposed Algorithm
Again, the IApriori displayed excellent execution time. The time required to mine the dataset by IApriori (support threshold = two) was 98 ms, while 395 and 605 ms for AprioriTID and Apriori, respectively. The next experimental work captures the ability of the IApriori Algorithm to process different dataset sizes, as well as the effect of dataset size on execution time. Additionally, the IApriori incorporated the changing threshold. Figure 6 presents the outcomes of mining experiments with a set of database sizes (1K, 5K, 20K, and 75K) (Management, 2009). The execution time was about the average time for 10 experiments for each threshold value, whereby the support threshold changed from 0% to 10%. It appeared that addition time was required for the first experiment only, which was 710 ms, for the dataset with 75K transactions. Figure 4: Experimental results for breast cancer dataset (execution time). Figure 5: Experimental results for Acute Inflammations dataset (execution time). The above figures portray the outstanding ability displayed by IApriori in substantially minimizing the execution time. This achievement was tested on various types of datasets across multidisciplinary domains, along with the standard case study for ARM, namely basket market analyses, medical, and game datasets mined by IApriori. The IApriori performed rather exceptionally well for different types and sizes of datasets, as well as for different values of support threshold parameter. Overall, IApriori has achieved remarkable outcomes. ### Comparison

This section discusses the theoretical comparison between IApriori and other algorithms. This comparison is divided into two parts: IApriori is compared with the baseline algorithm in the first part, while on the incremental domain in the second part. ### Apriori

The IApriori algorithm seemingly managed to enhance the Apriori algorithm, which is attributed to their shared similarities. The variances are as listed in the following:

* IApriori algorithm retrieved the data from the database once; it collected the first itemset. Subsequently, the algorithm collected the support count for the candidate itemset by intersecting the first itemset. The original algorithm had to check the database for every single itemset. * The Apriori algorithm applied the pruning strategy to exclude itemsets that did not satisfy the \(Minsupp\) threshold. In the proposed method, the algorithm retained all itemsets. * The Apriori algorithm built the whole knowledge-based over the specific threshold, and the output knowledge appeared to be invalid for other thresholds. Upon a change in the threshold, the mining had to restart from the beginning point of building valid knowledge. On the contrary, many instances can be arrived at using the proposed algorithm in the case of changing threshold. ### Incremental Algorithms

Many algorithms have been proposed in the field of incremental learning for ARM. They make sense in the process of solving standoff problems. The method proposed in this study presents an improved set of features. However, several variances were noted, as listed below:

Figure 6: Execution time (ms) under different thresholds for the same transaction size (Apriori dataset). * IApriori weighed in all manipulated data found in the database, including added, updated, and deleted data. On the contrary, the remaining algorithms that dealt with the incremental issue considered only the add operation, while neglecting both update and delete operations. * As for the other algorithms, there were cases in which the algorithm had to rebuild the itemset from scratch. This, nonetheless, was not required for the proposed algorithm. * IApriori offered credible support and confidence for itemset and rule set, respectively. IApriori updated the support value, thus reflecting the itemset to the database transactions. Additionally, IApriori filtered the itemset within the changeable threshold setting. The next table (Table 20) summarizes the features of the reviewed incremental ARM Algorithms.