In our previous blog, MachineX: Layman's Guide to Association Rule Learning, we discussed what Association rule learning is all about, and as you can already tell, with a large dataset, which almost every market has, finding association rules isn't very easy. For these purposes, we introduced measures of interestingness, which were support, confidence, and lift. Support tells us how frequent an itemset is in a given dataset and confidence tells us about the reliability of that rule. Due to large datasets, it is computationally very expensive to find both support and confidence for it.
An initial step towards improving the performance of association rule mining algorithms is to decouple the support and confidence requirements. Let's look at it more closely.
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