Enter transaction counts or supports into the calculator to compute support, confidence, lift, leverage, conviction, and a plain language interpretation for the association rule X → Y.
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Association Rule Formula
The following formula is used to calculate the confidence of the association rule X → Y in a database.
\mathrm{conf}(X \rightarrow Y)=\frac{S(X \cap Y)}{S(X)}Variables:
- conf(X → Y) is the confidence of the rule X → Y
- X is the antecedent itemset
- Y is the consequent itemset
- S(X ∩ Y) is the support of the intersection of itemsets X and Y (the fraction of transactions containing both X and Y)
- S(X) is the support of itemset X (the fraction of transactions containing X)
To calculate confidence, first determine the support of the intersection of itemsets X and Y (X ∩ Y), which represents the fraction of transactions containing both X and Y. Then, determine the support of itemset X. Divide S(X ∩ Y) by S(X). The result is the confidence of the association rule X → Y, which corresponds to the conditional probability P(Y | X) when supports are computed as proportions of transactions.
What is an Association Rule?
Association rule learning is a rule-based machine learning and data mining technique used to discover relationships between variables in large datasets. An association rule is typically written as X → Y, where X and Y are itemsets. Rules are commonly evaluated using measures such as support, confidence, and lift. This technique is widely used in market basket analysis to find associations between products purchased by customers.
