Association Rules is one of the very important concepts of machine learning being used in market basket analysis. In a store, all vegetables are placed in the same aisle, all dairy items are placed together and cosmetics form another set of such groups. Investing time and resources on deliberate product placements like this not only reduces a customer’s shopping time, but also reminds the customer of what relevant items (s)he might be interested in buying, thus helping stores cross-sell in the process. Association rules help uncover all such relationships between items from huge databases. One important thing to note is- Rules do not extract an individual’s preference, rather find relationships between set of elements of every distinct transaction. This is what makes them different from collaborative filtering.
Antecedent - Pre-existing.
Consequent - Following as a result or effect of Antecedent.
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Support. (Joint-Probability)
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Confidence. (Conditional-probabilty)
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Lift. (Is Antecedenent driving Consequent)