Association analysis is the process of finding frequent patterns, associations, correlations, or causal structures between sets of items or objects in transaction data, relational data, or other information carriers.

Association analysis method

Apriori algorithm

The Apriori algorithm is a basic algorithm for mining frequent itemsets required to generate Boolean association rules. It uses an iterative method called layer-by-layer search, where k itemsets are used to explore k+1 itemsets.

FP-growth algorithm

The FP-growth algorithm scans the transaction database twice and compresses and stores the frequent items contained in each transaction in descending order of their support into the FP-tree. In this way, in the process of discovering frequent patterns in the future, there is no need to scan the transaction database again, and only the search in the FP-Tree is required.

Association rule learning

Association rule learning is a method for discovering interesting relationships between variables in a large database. Its goal is to use some interestingness measures to identify strong rules found in the database.