Association analysis is often used to discover implicit associations between items in large datasets, and the discovered associations can be expressed in the form of association rules. Association rules are often used to find new cross-selling opportunities. RuleMining2A uses two classic association analysis algorithms, Apriori and FP-Growth, to assist you in finding association rules between items in the dataset.

For example, the rule {pasta} ⇒ {shrimp} found from sales data means that customers who purchase pasta are also likely to purchase shrimp. This information can be used as the basis for marketing decisions such as product recommendations, promotional pricing, and product placement.

Apriori Algorithm:
Association analysis first needs to find frequent itemsets, and then use these frequent itemsets to obtain association rules. The Apriori algorithm is one of the most famous frequent itemset mining algorithms and is often used in recommender systems and market basket analysis.

FP-Growth Algorithm:
The FP-Growth algorithm is another popular algorithm for mining frequent itemsets. The FP-Growth algorithm is based on the Apriori algorithm principle, but uses a method called FP-tree to find frequent itemsets. The FP-Growth algorithm generally exhibits more efficient performance when dealing with large datasets.

The FP-Growth algorithm performs faster than the Apriori algorithm for some types of datasets. You can run your dataset with both algorithms and pick the one that performs faster.