Frequent Pattern Growth (FP-Growth)

FP-Growth is an algorithm used to identify common patterns or groups of items frequently bought together in large datasets, like shopping transactions. It efficiently compresses data into a structure called an FP-tree, capturing item relationships without exhaustive searches. By analyzing this tree, it quickly uncovers frequent combinations without generating all possible itemsets. This makes it faster and more resource-efficient than older methods like Apriori. Essentially, FP-Growth helps businesses understand customer behaviors and trends by revealing which items are often purchased together, enabling smarter marketing and inventory decisions.