machine learning - Prediction of sets -

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assume have 4 transactions a,a,a,b. assume, has been grouped b single group. there similar transactions c,c,g,c,c,c , c,c,b,c , c,c,b,c,c grouped. here see c has been grouped b , g c&b have been grouped twice compared c&g there lots of transactions these.

when new set of transactions come c,c,b,a,a,g need suggest each of these transactions, other transactions should paired with. in example, prediction should follows.

c -> b (predicted since had max probability) c -> b (predicted since had max probability) b -> c (predicted) -> b (predicted) -> b (predicted) g -> c (predicted)

1) simple case approach/ ml technique needed achieve this? 2) if transactions become more complicated a,a,a,b,n grouped, how able handle case.

i can go through transactions , calculate probability of each transactions in group, there better approach should considering. packages available these problems?


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