Frequent Pattern Growth

Frequent Pattern Growth (FP-Growth) is an algorithm used to identify common patterns or groups in large datasets, such as items frequently bought together. Instead of checking every possible combination directly (which is slow), it creates a compact structure called an FP-tree that summarizes the data’s patterns. This tree allows the algorithm to efficiently find all frequent itemsets—groups of items that appear together often—by exploring relevant parts of the structure. FP-Growth is faster and more memory-efficient than earlier methods, making it useful for analyzing large-scale data in fields like market basket analysis, web usage, and recommendation systems.