# Frequent Itemset

A frequent itemset is a dataset subset with an itemset frequency that is greater than some given proportion threshold.

## References

### 2004

• (Sivanandam et al., 2004) ⇒ S. N. Sivanandam, D. Sumathi, T. Hamsapriya, and K. Babu. (2004). “Parallel Buddy Prima – A Hybrid Parallel Frequent itemset mining algorithm for very large databases." Retrieved from http://www.acadjournal.com.
• QUOTE: The definition of a frequent pattern relies on the following considerations. A set of items is referred to as an itemset (pattern). An itemset that contains $k$ items is a k-itemset. The set {X, Y} is a 2- itemset. The occurrence frequency of an itemset is the number of transactions that contain the itemset. This is also known as the frequency or the support count of an itemset. An itemset satisfies minimum support if the occurrence frequency of the itemset is greater than or equal to the minimal support threshold value defined by the user. The number of transactions required for the itemset to satisfy minimum support is therefore referred to as the minimum support count. If an itemset satisfies minimum support, then it is a frequent itemset (frequent pattern).

A frequent itemset is called closed if it does not have any superset with the same support. A frequent itemset is said to be maximal if it has no supersets that are frequent. The collection of maximal frequent itemsets is a subset of the collection of closed frequent itemsets, which is a subset of the collection of all frequent itemsets. Maximal frequent itemsets are necessary for generating association rules.