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Data Applied: Association
 

Data Applied: Association

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Data Applied: Association

Data Applied: Association

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    Data Applied: Association Data Applied: Association Presentation Transcript

    • Data-Applied.com: Association
    • 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
    • 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
    • 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
    • Associations using Data Applied’s web interface
    • Step1: Selection of data
    • Step2: Selecting Association
    • Step3: Result
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