In order to use Apriori, generation of candidate item sets is required. If the itemset in the dataset is particularly vast, the number of instances of these item sets could be rather high. Apriori must perform repeated checks of the database in order to determine whether or not each newly formed itemset is supported, which results in increased expenses. Due to this, it is ineffective when utilised with a significant amount of datasets. For instance, the case in which there is a frequent-one itemset that has 104 elements from the set. The Apriori algorithm must generate more than 107 candidates with a two-length, whose will be assessed and accumulated as a result of their participation in the process. The use of the Apriori technique would be the generation of 2100 probable sets of items with the objective to identify a frequent sequence with size 100 (including v1, v2, v3, v4..., v100). The result would be an illustration of how the process works.

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