Oracle Apriori

The Apriori algorithm discovers association rules in data. For example, "if a customer purchases a razor and after shave, then that customer will purchase shaving cream with 80% confidence." The association mining problem can be decomposed into two subproblems:

• Find all combinations of items, called frequent itemsets, whose support is greater than the minimum support.
• Use the frequent itemsets to generate the desired rules. The idea is that if, for example, ABC and BC are frequent, then the rule "A implies BC" holds if the ratio of support(ABC) to support(BC) is at least as large as the minimum confidence. Note that the rule will have minimum support because ABCD is frequent. ODM Association only supports single consequent rules (ABC implies D).

The number of frequent itemsets is governed by the minimum support parameters. The number of rules generated is governed by the number of frequent itemsets and the confidence parameter. If the confidence parameter is set too high, there may be frequent itemsets in the association model but no rules.

ODM uses an SQL-based implementation of the Apriori algorithm. The candidate generation and support counting steps are implemented using SQL queries. Specialized in-memory data structures are not used. The SQL queries are fine-tuned to run efficiently in the database server by using various hints.