A set of items such as Keyboard and Mouse in the previous example is referred to as an itemset. An itemset containing
k items is called k-itemset, e.g., {Keyboard, Mouse} is a 2-itemset.
Association rule mining is to
- Find all frequent itemsets:
Suppose
min_supis the minimum support threshold. An itemset satisfies minimum support if the occurrence frequency of the itemset is greater than or equal tomin_sup. If an itemset satisfies minimum support, then it is a frequent itemset. - Generate strong association rules from the frequent itemsets: Rules that satisfy both a minimum support threshold and a minimum confidence threshold are called strong. These rules must be the frequent itemsets and strong.
| Apriori is a classic algorithm for finding all association rules. Apriori algorithm is an influential algorithm for mining frequent itemsets for Boolean association rules. |
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| The Apriori algorithm shown in the figures is complicated. Instead of studying it directly, two of its application examples will be given next to help the understanding. |
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