In its most common form, association rule learning involves a collection of transactions. For each transaction, there is a set of possible items present. If an item is present in a transaction, then $1$ is denoted, else $0$.

Association rule learning then attempts to create "association rules" whereby one can essentially predict if a person buys onions and potatoes, then the will buy hamburgers

$\lbrace\text{onions,potatoes}\rbrace \implies \lbrace\text{hamburger}\rbrace$


Can't a Pearson correlation between binary metrics yield the same information? What does association rule learning offer that is different from Pearson's correlation?

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    $\begingroup$ Correlation is a measure of how linearly related two vectors are. In your example, onions, potatoes and hamburger is a set of three entities. Correlation cannot directly be used to yield the same result. However, you can perform pairwise correlations on the binary variables. $\endgroup$ – Arun Jose Nov 16 '16 at 7:32

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