The Fl = {a, b, c, d, e} is set by the frequent 1-itemsets. Fig. 1 displays a PPC-tree. The node with (4, 5) indicates that the item name is c, the count is 4, and the pre-order is 4. is displayed in Fig. 3. The node in the lower left corner of Figure 3 illustrates the itemset "bceaf" and registers item b. SS 9 a A new and improved itemset representation has been created for frequent itemset mining. We present a successful method for mining frequent itemsets based on the DiffNodesets structure: DiffNodesets. Sometimes, DiffNodesets can achieve great efficiency by just enumerating frequently occurring itemsets without creating candidates. In other scenarios, to find frequently occurring itemsets, DiffNodesets employs a hybrid search strategy combined with a set enumeration tree. Fig. 4. Running time on the mushroom dataset he post algorithm shows running time performance like the Fin method up to the min sup criterion of 1%. In this sense, the suggested algorithm is better than the others. The datasets in Table 2 are subjected to memory consumption tests in this part, much like in running time experiments, where test parameter settings are the same as in previous running time experiments. Figs. 8, 9, 10, and 11 display the memory utilization results from the experiment. Fig. 8 illustrates the memory required to store the user input data for the Fin, PrePost, DiffNodesets, and EN-list algorithms on the mushroom datasets. Mushroom is a relatively small dataset, and the RAM requirements of Prepost and DiffNodesets are likewise modest. The Prepost approach still requires a large amount of RAM to mine frequently occurring itemsets, even with a lower threshold. The least amount of memory is used by the EN-list method for each minute period (5%, 10%, 15%, 20%, 25%). The EN-list and DiffNodesets techniques demand less memory than the Prepost algorithm when the min sup is lower. The memory utilization resulting from the EN-list, Fin, PrePost, and DiffNodesets of the chess Jatasets is shown in Fig. 9. The performance of the Prepost algorithm drastically deteriorates as the threshold alls below 15%. The EN-list technique guarantees the best possible memory use for the threshold settings and Jataset that are supplied. The memory results for the EN-list, Fin, PrePost, and DiffNodesets algorithms for the Fig. 5. Running time on the Chess dataset Kosarak datasets are shown in Fig. 10. Out of all of them, the EN-list method offers the most dependable anc effective memory performance. Prepost encounters memory overflow at almost every threshold value. Fig. 11 displays the memory required for the EN-list, Fin, PrePost, and DiffNodesets algorithms on the pumsb datasets Furthermore, the EN-list method has the most efficient and reliable memory performance out of all of the alternatives. Post encounters memory overflow with respect to the entire threshold value. The PPC tree is a special kind of tree structure that the PrePost algorithm uses. There are usually more transactions than nodes in the PPC tree when using the N list structure, which is what the Postal Algorithm employs. Therefore, the EN-lis approach usually requires less memory than the Fin, PrePost, and DiffNodesets algorithms. These experiments s how that the best technique for mining common itemsets is EN-list, which requires the least amount of memory for all min sup. Fig. 9. Memory consumption on the Chees dataset Fig. 10. Memory consumption on the Kosarak dataset Fig. 11. Memory consumption on the pumsb dataset
