2. Introduction Definition: An association rule can predict any number of attributes and also any combination of attributes They map relationships between attributes Parameter for selecting an Association Rule: Coverage: The number of instances they predict correctly Accuracy: The ratio of coverageand total number of instances the rule is applicable We want association rule with high coverage and atleast minimum specified accuracy
3. Terminology used Terminology: Item – set: A combination of attributes Item: An attribute – value pair An example: For the weather data we have a table with each column containing an item – set having different number of attributes With each entry the coverage is also given The table is not complete, just gives us a good idea
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5. Generation of association rules We need to specify a minimum coverage and accuracy for the rules to be generated before hand Steps: Generate the item sets Each item set can be permuted to generate a number of rules For each rule check if the coverage and accuracy is appropriate This is how we generate association rules
