Frequent Pattern Growth

Frequent Pattern Growth (FP-Growth) is a data mining algorithm used for discovering frequent itemsets in transactional databases without generating candidate itemsets. It works by constructing a compact data structure called an FP-tree (Frequent Pattern tree) to represent the dataset, then recursively mining this tree to extract patterns. This approach is more efficient than traditional methods like Apriori, especially for large datasets with many transactions.

Also known as: FP-Growth, FP Growth, Frequent Pattern Mining, FP-tree algorithm, Frequent Itemset Mining

Why learn Frequent Pattern Growth?

Developers should learn FP-Growth when working on association rule mining tasks, such as market basket analysis, recommendation systems, or anomaly detection in large-scale data. It is particularly useful in scenarios where performance is critical, as it reduces computational overhead by avoiding the candidate generation step, making it faster and more scalable for high-dimensional data.

Learning Resources

• tutorial: FP-Growth Algorithm Explained
• tutorial: FP-Growth in Data Mining
• course: FP-Growth Algorithm - Coursera Course
• docs: FP-Growth Implementation in Python
• video: FP-Growth vs Apriori - Comparison Video