FP-Growth is an unsupervised machine learning technique used for association rule mining which is faster than apriori. However, it cannot be used on large datasets due to its high memory requirements. More information about it can be found here.
You can learn more about FP-Growth algorithm in the below video.
The below code will help you to run FP-Growth in Python
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# library installation for FP-Growth #!pip install pyfpgrowth # Sample code to do FP-Growth in Python import pyfpgrowth # Creating Sample Transactions transactions = [ ['Milk', 'Bread', 'Saffron'], ['Milk', 'Saffron'], ['Bread', 'Saffron','Wafer'], ['Bread','Wafer'], ] #Finding the frequent patterns with min support threshold=0.5 FrequentPatterns=pyfpgrowth.find_frequent_patterns(transactions=transactions,support_threshold=0.5) print(FrequentPatterns) # Generating rules with min confidence threshold=0.5 Rules=pyfpgrowth.generate_association_rules(patterns=FrequentPatterns,confidence_threshold=0.5) Rules |
Sample Output
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