Question

Most frequent pattern mining algorithms consider only distinct items in a transaction. However, multiple occurrences of an item in the same shopping basket, such as four cakes and three jugs of milk, can be important in transactional data analysis. How can one mine frequent itemsets efficiently considering multiple occurrences of items? Propose modifications to the well-known algorithms, such as Apriori and FP-growth, to adapt to such a situation.

Answer 1

These best mothodology to accomplish the goal is to move to a slightly less efficient system of human based product review. For example, it is reasonable to think thag multiple repeat items in a set cannot be easily understood my current systems. This gap could be bridged by installing a human form of quality assurance to ensure that such "tally marks" are made and enable correct understanding Of the data at hand.