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Consider the following data set: The above table shows the quantity of each item used in the orders SO1 SO2 etc. I need to club the orders in such a way that maximum number of items are common amongst them. For example:

data

SO1 SO3 SO5 can be clubbed for 8 items ( Item 1,2,3,5,6,8,9,11) and SO2 and SO4 can clubbed together because 9 items are common ( Item 1,3,4,5,6,8,9,10,11).

The approach I followed was: I found out the number of combinations possible as in (SO1 SO2), (SO1 SO3), (SO1 SO4)……(SO1 SO2 SO3), (SO1 SO2 SO4),….(SO1 SO2 SO3 SO4),…(SO1 SO2 SO3 SO4 SO5). For n number of SOs I would be getting around 2^n -n -1 combinations. Later I compared the data in each combination for equality.

Based on the number of matches, I thought I would be able to select the combinations. But this process would become cumbersome for 300 shop orders with around 2000 items. And it is taking a lot of time to compute as well.

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Have you tried using frequent itemset mining?

It finds the most frequent item combinations.

The key idea is that when item A is not frequent, you don't need to further explore any combinations including A.

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  • $\begingroup$ In frequent itemset mining it only cares about whether the item is present or not in the order.It does not consider the quantitiy of the item needed. $\endgroup$
    – learner_78
    May 29, 2019 at 9:20
  • $\begingroup$ Not have you explained if it matters, and how it should be taken into account. There are ways to enhance FIM with quantity information - it's usually just so expensive that you'd rather analyze this in postprocessing. $\endgroup$
    – Has QUIT--Anony-Mousse
    May 29, 2019 at 21:39

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