Machine Learning with Swift

Using association measures to assess rules

Look at these two rules:

• {Oatmeal, corn flakes → Milk}
• {Dog food, paperclips → Washing powder}

Intuitively, the second rule looks more unlikely than the first one, doesn't it? How can we tell that for sure, though? In this case, we need some quantitative measures that will show us how likely each rule is. What we are looking for here are association measures, as we call them in machine learning and data mining. Rule mining algorithms revolve around this notion in a similar manner to how distance-based algorithms revolve around distance metrics. In this chapter, we're going to use four association measures: support, confidence, lift, and conviction (see Table 5.1).

Note that these measures tell ...