Support for an itemset X in a transactional database D is defined as count(X) / |D|.
For an association rule X Þ Y, we can calculate
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support(X Þ Y) = support(XY) = support(X union Y). |
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confidence(X Þ Y) = support(XY) / support(X). |
Support (S) and Confidence (C) can also be related to joint probabilities and conditional probabilities as follows.
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support(X Þ Y) = P(XY). |
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confidence(X Þ Y) = P(Y/X). |
The number of association rules that can be derived from a dataset D are exponentially large. Interesting association rules are those whose support and confidence are greater than minSupp and minConf.
Frequent itemsets (also called as large itemsets), are those itemsets whose support is greater than minSupp. The apriori property (downward closure property) says that any subsets of an frequent itemset are also frequent itemsets.
