3 association algorithms

Many algorithms for generating association rules were presented over time.

Some well known algorithms are Apriori, Eclat and FP-Growth, but they only do half the job, since they are algorithms for mining frequent itemsets. Another step need to be done after to generate rules from frequent itemsets found in a database.

[edit]Apriori algorithm
Main article: Apriori algorithm
Apriori[5] is the best-known algorithm to mine association rules. It uses a breadth-first search strategy to counting the support of itemsets and uses a candidate generation function which exploits the downward closure property of support.

[edit]Eclat algorithm
Eclat[6] is a depth-first search algorithm using set intersection.

[edit]FP-growth algorithm
FP-growth (frequent pattern growth)[16] uses an extended prefix-tree (FP-tree) structure to store the database in a compressed form. FP-growth adopts a divide-and-conquer approach to decompose both the mining tasks and the databases. It uses a pattern fragment growth method to avoid the costly process of candidate generation and testing used by Apriori.

in reference to: HimmiH (view on Google Sidewiki)