Définition
An a priori algorithm helps to find association rules: which products, events or features often appear together. Its classic example is the analysis of shopping baskets. The algorithm first looks for frequently occurring sets of elements, and then builds rules of the form “if you bought A, you often buy B.”
Exemple
The store sees that customers who grab coffee often buy creamer, and uses this to make recommendations or display products.
Pourquoi c'est important
The term is important for analytics, recommendations, marketing and primary data research without complex neural networks.
Fonctionnement
The algorithm uses the principle: if a set of elements is rare, then all its extended sets cannot be frequent either. This reduces the number of checks.
Où c'est utilisé
- basket analysis
- product recommendations
- finding connections in data
Limites
The method can be heavy on large data and produce many obvious rules. We need the right thresholds of support and credibility.
FAQ
Why is “Apriori Algorithm” useful to know?
The term is important for analytics, recommendations, marketing and primary data research without complex neural networks.