Apriori is an algorithm for frequent item set mining and association rule learning over relational databases.
It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
Here we try to find the rules of associations between antecedents(subject items) and consequents(recommended items).
So, unlike any traditional learning we do not explicitly provide set of rules.
We let the Deep Net model figure out the rules by process called "rules-mining"
Here we have used Grocery dataset to recommend items to customers in various countries
- Most modern retail stores like Walmart have this feature
- Your music platforms like Spotify uses this algorithm to recommend songs and playlists
- When criminal records are processed, crime agencies can figure out next course of action