Association Rules and the Apriori Algorithm: A Tutorial
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Originally published: 26/01/2018 14:13
Last version published: 30/01/2018 15:22
Publication number: ELQ-46325-2
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Association Rules and the Apriori Algorithm: A Tutorial

Tutorial on the Apriori Algorithm and the main Concepts of Association Rules.

Description
Association rules are statements in an 'If/Then' format that can help you to identify relationships between data that appears unrelated. An example of an association rule would be that "If a customer buys a loaf of bread, she is 80% likely to also buy milk."

An 'if' (the antecedent) and a 'then' (the consequent) are the two parts needed to make up an association rule. You will find the antecedent in the data, whilst the consequent is found in combination with the antecedent.

The Association rules are formed after you have analysed data to find common 'if/then' patterns and using the criteria of confidence and support to uncover the most important relationships. 'Support' refers to how frequently the items come up in the database, whilst 'confidence' refers to how often the 'if/then' statements have proven to be correct.

In data mining, associations rules are used to analyze and predict customer behaviour. They are used frequently in product clustering, shopping basket data analysis, store layout, and catalog design.

This tutorial explains more about association rules and the Apriori Algorithm using easy-to-read diagrams and good examples.

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