Process
Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. Association rule generation is usually split up into two separate steps:
- First, minimum support is applied to find all frequent itemsets in a database.
- Second, these frequent itemsets and the minimum confidence constraint are used to form rules.
While the second step is straightforward, the first step needs more attention.
Finding all frequent itemsets in a database is difficult since it involves searching all possible itemsets (item combinations). The set of possible itemsets is the power set over and has size (excluding the empty set which is not a valid itemset). Although the size of the powerset grows exponentially in the number of items in, efficient search is possible using the downward-closure property of support (also called anti-monotonicity) which guarantees that for a frequent itemset, all its subsets are also frequent and thus for an infrequent itemset, all its supersets must also be infrequent. Exploiting this property, efficient algorithms (e.g., Apriori and Eclat) can find all frequent itemsets.
Read more about this topic: Association Rule Learning
Famous quotes containing the word process:
“When you start with a portrait and search for a pure form, a clear volume, through successive eliminations, you arrive inevitably at the egg. Likewise, starting with the egg and following the same process in reverse, one finishes with the portrait.”
—Pablo Picasso (1881–1973)
“Consumer wants can have bizarre, frivolous, or even immoral origins, and an admirable case can still be made for a society that seeks to satisfy them. But the case cannot stand if it is the process of satisfying wants that creates the wants.”
—John Kenneth Galbraith (b. 1908)
“A man had better starve at once than lose his innocence in the process of getting his bread.”
—Henry David Thoreau (1817–1862)