Introduction to Data Mining

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Lesson 6.6

APRIORI Algorithm

 

The major steps in association rule mining are:

  1. Frequent Itemset generation

  2. Rules derivation

The APRIORI algorithm uses the downward closure property, to prune unnecessary branches for further consideration. It needs two parameters, minSupp and minConf. The minSupp is used for generating frequent itemsets and minConf is used for rule derivation.

 

The APRIORI algorithm:

  1. k = 1;

  2. Find frequent itemset, Lk from Ck, the set of all candidate itemsets;

  3. Form Ck+1 from Lk;

  4. k = k+1;

  5. Repeat 2-4 until Ck is empty;

Step 2 is called the frequent itemset generation step. Step 3 is called as the candidate itemset generation step. Details of these two steps are in the next lesson.

 

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