How FP Growth algorithm works? | AI ML tutorials by a Data Scientist | Thinking Neuron

https://thinkingneuron.com/data-science-interview-questions-for-it-industry-part-4-unsupervised-ml/#fp-growth FP-Growth stands for Frequent Patterns Growth. This algorithm is faster than apriori but not suitable for large datasets due to high memory requirements. Like Apriori and ECLAT, FP-Growth also generates rules in two steps: 1. Find out the frequent combination of items called “itemsets” which satisfy the minimum support threshold. 2. Generate association rules from frequent itemsets based on the minimum confidence threshold. The main difference between apriori and FP-Growth is the way FP-Growth FINDS frequent itemsets. FP-Growth algorithm uses a special tree data structure known as “FP-Tree” that stores the frequent patterns at one place This tree helps to reduce the need for scanning databases dramatically! In only two scans, all the frequent itemsets are found. Once all the frequent itemsets are found, then the rules are generated using the tree.