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Have processed product sales data after data transformation(map 0&1's respective product and transaction) through Apriori alogorithm.Output associatiob rules have some product mapings between frequently bought products and their lift was > 1.These association rules were generated through by manually feeding various min supp values and choosen the one which resulted higher association rules.

Have done random check manually on the output,some of the products,antecedents& consequents repeated heavily and few product association makes sense and few do not.

So,how to ensure association rules that were generated for all frequently bought together products in a big dataframe and they are sensible and optimal?

Is there a better way to tune and pick suitable min supp value automatically accordance with number of transactions in a big dataframe?

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