The Apriori Algorithm is commonly used for frequent pattern mining, and FP-growth is an upgraded version of it (AKA Association Rule Mining). It's an analytical technique for identifying common patterns or correlations in data sets.
False Positives (FP): 1
False Negatives (FN): 4.
The data structure of the FP-growth technique for mining frequent itemsets from a database using association rules is the FP-tree (Frequent Pattern tree). It's a great replacement for the apriori algorithm. Most earlier studies have focused on extracting patterns from a database.