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I have a dataset as (var1, var2, out), where the ordered pair <var1, var2> gives out. Most of the frequent pattern mining algorithms like the Apriori and FP growth algorithms do not preserve the order of var1 and var2.
Which are some of the available pattern mining algorithms (maybe also a NN trick), to find association rules between ordered pair <var1, var2> and output variable out?
Assuming you only have these two features (var1, var2), you might want to:
* Create one-hot encoded features for each variable under each position.
* Add a column on which variable is first (e.g. two columns - likely to work with trees but not with anything else).
* Take each possible combination of variables and use that as your only input (e.g. you then take the average of out for that combination, perhaps adding some prior or smoothing).
As the comments mentioned, if out is some discrete event, maybe you'd want to instead look at Markov models.
Sequence mining seems the thing you are looking for. Used quite extensively in problems with ordered data, e.g. mapping DNA sequence -> disease. There are algorithms like SPADE and PrefixSpan.
