The Apriori Algorithm-DWDM смотреть онлайн

The Apriori Algorithm
A Candidate Generation-and-Test Approach in Association Rule Mining
Finding all frequent item sets.
Apriori: To find frequent item sets using an iterative level-wise approach based on candidate generation.
SCAN the Data Set - Set of frequent 1-item set is found, which satisfy minimum support and the resulting set is L1 (Level-1).
L1 is used to find L2, a set of frequent 2-item set which is used to find L3 and so on until no more frequent itemsets found.

Generating Association Rules form Frequent Itemsets
After finding frequent Itemsets form DB, generate strong association rules
It satisfy both minimum support and minimum confidence.
For each frequent itemset l, generate all non empty subsets of l,
For every non empty subsets of l, output the rule
s = (l-s)
Confidence
The non empty subsets of l are
{l1,l2}, {l1,l5}, {l2,l5}, {l1}, {l2},{l5}
The resulting association rules with confidence
l1 ˄ l2 → l5 confidence = 2/4 = 50%
l1 ˄ l5 → l2 confidence = 2/2 = 100%
l2 ˄ l5 → l1 confidence = 2/2 = 100%
l1 → l2 ˄ l5 confidence = 2/6 = 33%
l2 → l1 ˄ l5 confidence = 2/7 = 29%
l5 → l1 ˄ l2 confidence = 2/2 = 100%
Let the minimum confidence threshold is 70%,
then only the 2nd , 3rd and last rules above are output.
The confidence should be above threshold.

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