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:
“Experiences in order to be educative must lead out into an expanding world of subject matter, a subject matter of facts or information and of ideas. This condition is satisfied only as the educator views teaching and learning as a continuous process of reconstruction of experience.”
—John Dewey (18591952)
“A process of genocide is being carried out before the eyes of the world.”
—Pope John Paul II (b. 1920)
“A designer who is not also a couturier, who hasnt learned the most refined mysteries of physically creating his models, is like a sculptor who gives his drawings to another man, an artisan, to accomplish. For him the truncated process of creating will always be an interrupted act of love, and his style will bear the shame of it, the impoverishment.”
—Yves Saint Laurent (b. 1936)