Seminar Association Rules

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ali alrazgi ,علي الرازقي
Association Rules

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Seminar Association Rules

  1. 1. Seminar Association Rules Mining-Apriori
  2. 2. <ul><li>Study on Application of Apriori Algorithm in Data Mining </li></ul><ul><li>2010 Second International Conference on Computer Modeling and Simulation </li></ul><ul><li>Yanxi Liu </li></ul><ul><li>© 2010 IEEE </li></ul>
  3. 3. <ul><li>Association rule mining: </li></ul><ul><ul><li>Finding frequent patterns, associations, correlations, or </li></ul></ul><ul><li>Applications: </li></ul><ul><ul><li>Basket data analysis, cross-marketing, catalog design, loss-leader analysis, clustering, classification, etc. </li></ul></ul><ul><li>Examples. </li></ul><ul><ul><li>Rule form: “ Body  ead [support, confidence]”. </li></ul></ul><ul><ul><li>buys(x, “diapers”)  buys(x, “beers”) [0.5%, 60%] </li></ul></ul><ul><ul><li>major(x, “CS”) ^ takes(x, “DB”)  grade(x, “A”) [1%, 75%] </li></ul></ul>
  4. 4. <ul><li>Given: (1) database of transactions, (2) each transaction is a list of items (purchased by a customer in a visit) </li></ul><ul><li>Find: all rules that correlate the presence of one set of items with that of another set of items </li></ul><ul><ul><li>E.g., 98% of people who purchase tires and auto accessories also get automotive services done </li></ul></ul><ul><li>Applications </li></ul><ul><ul><li>*  Maintenance Agreement (What the store should do to boost Maintenance Agreement sales) </li></ul></ul><ul><ul><li>Home Electronics  * (What other products should the store stocks up?) </li></ul></ul><ul><ul><li>Attached mailing in direct marketing </li></ul></ul><ul><ul><li>Detecting “ping-pong”ing of patients , faulty “collisions” </li></ul></ul>
  5. 5. <ul><li>Apriori algorithm is one of the most influential </li></ul><ul><li>algorithms to mine the frequent item sets of Boolean association rules. </li></ul>
  6. 7. <ul><li>Mining Association Rules Using Fast Algorithm </li></ul><ul><li>M.Anandhavalli & Sandip Jain </li></ul><ul><li>© 2010 IEEE </li></ul>
  7. 8. <ul><li>Efficiently Using Matrix in Mining Maximum Frequent Itemset </li></ul><ul><li>2010 Third International Conference on Knowledge Discovery and Data Mining </li></ul><ul><li>Liu Zhen-yu </li></ul><ul><li>© 2010 IEEE </li></ul>
  8. 9. <ul><li>An Encounter with Strong Association Rules </li></ul><ul><li>G. S. Bhamra </li></ul><ul><li>© 2010 IEEE </li></ul>
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  11. 12. Application

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