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Data Applied: Association


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Data Applied: Association

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Data Applied: Association

  1. 1. Association<br />
  2. 2. Introduction<br />Definition:<br />An association rule can predict any number of attributes and also any combination of attributes<br />They map relationships between attributes<br />Parameter for selecting an Association Rule:<br />Coverage: The number of instances they predict correctly<br />Accuracy: The ratio of coverageand total number of instances the rule is applicable<br />We want association rule with high coverage and atleast minimum specified accuracy<br />
  3. 3. Terminology used<br />Terminology:<br />Item – set: A combination of attributes<br />Item: An attribute – value pair<br />An example:<br />For the weather data we have a table with each column containing an item – set having different number of attributes<br />With each entry the coverage is also given<br />The table is not complete, just gives us a good idea<br />
  4. 4.
  5. 5. Generation of association rules<br />We need to specify a minimum coverage and accuracy for the rules to be generated before hand<br />Steps:<br />Generate the item sets<br />Each item set can be permuted to generate a number of rules<br />For each rule check if the coverage and accuracy is appropriate<br />This is how we generate association rules<br />
  6. 6. Associations using Data Applied’s web interface<br />
  7. 7. Step1: Selection of data<br />
  8. 8. Step2: Selecting Association<br />
  9. 9. Step3: Result<br />
  10. 10. Visit more self help tutorials<br /><ul><li>Pick a tutorial of your choice and browse through it at your own pace.
  11. 11. The tutorials section is free, self-guiding and will not involve any additional support.
  12. 12. Visit us at</li>