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The FP-Growth (Frequent Pattern Growth) algorithm is a powerful method for mining frequent itemsets from large transactional datasets. Unlike the Apriori algorithm, it avoids candidate generation and multiple database scans by using an efficient tree structure called the FP-Tree.
Below is a Python implementation of the FP-Growth algorithm:
from collections import defaultdictclass TreeNode:def __init__(self, name, count, parent):self.name = nameself.count = countself.parent = parentself.children = {}self.link = Nonedef increment(self, count):self.count += countdef construct_fp_tree(transactions, min_support):header_table = defaultdict(int)for transaction in transactions:for item in transaction:header_table[item] += 1# Remove items below min_supportheader_table = {k: v for k, v in header_table.items() if v >= min_support}if not header_table:return None, None# Sort items by frequencysorted_items = sorted(header_table.items(), key=lambda x: (-x[1], x[0]))header_table = {k: [v, None] for k, v in sorted_items}root = TreeNode("Null", 1, None)for transaction in transactions:filtered_items = [item for item in transaction if item in header_table]filtered_items.sort(key=lambda x: -header_table[x][0])update_tree(filtered_items, root, header_table)return root, header_tabledef update_tree(items, node, header_table):if items:first_item = items[0]if first_item in node.children:node.children[first_item].increment(1)else:new_node = TreeNode(first_item, 1, node)node.children[first_item] = new_node# Update header tableif not header_table[first_item][1]:header_table[first_item][1] = new_nodeelse:current_node = header_table[first_item][1]while current_node.link:current_node = current_node.linkcurrent_node.link = new_nodeupdate_tree(items[1:], node.children[first_item], header_table)def mine_fp_tree(header_table, min_support):patterns = {}for item in sorted(header_table.keys(), key=lambda x: header_table[x][0]):patterns[item] = []conditional_patterns = []# Traverse linked list to find conditional patternsnode = header_table[item][1]while node:path = []parent = node.parentwhile parent and parent.name != "Null":path.append(parent.name)parent = parent.parentpath.reverse()for _ in range(node.count):conditional_patterns.append(path)node = node.link# Recursively mine conditional FP-treeconditional_tree, conditional_header = construct_fp_tree(conditional_patterns, min_support)if conditional_header:sub_patterns = mine_fp_tree(conditional_header, min_support)for sub_pattern, count in sub_patterns.items():patterns[item + "," + sub_pattern] = countreturn patterns# Example Usagetransactions = [['A', 'B', 'C'],['A', 'B', 'D'],['A', 'C', 'D'],['B', 'C', 'D']]min_support = 2fp_tree, header_table = construct_fp_tree(transactions, min_support)frequent_patterns = mine_fp_tree(header_table, min_support)print("Frequent Patterns:", frequent_patterns)Copied!âś•Copy Frequent Pattern Growth Algorithm - GeeksforGeeks
Jan 13, 2026 · Unlike the Apriori algorithm which suffers from high computational cost due to candidate generation and multiple database scans. FP-Growth avoids these inefficiencies by compressing the …
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Unlike the Apriori algorithm which suffers from high computational cost due to candidate generation and multiple database scans. FP-Growth avoids these ine…
Implement FP Growth Algorithm in Python - Coding Infinite
See more on codinginfinite.comWe will use the mlxtend module in Python to implement the fp growth algorithm. It provides us with the fpgrowth() function to calculate the frequent itemsets and the association_rules() function for association rule mining. Before implementing the fp growth algorithm, I suggest you read this article on thefp growth algorithm numerical ex…enaeseth/python-fp-growth - GitHub
This module provides a pure Python implementation of the FP-growth algorithm for finding frequent itemsets. FP-growth exploits an (often-valid) assumption that …
FP Growth: Frequent Pattern Generation in Data Mining …
Oct 30, 2020 · FP Growth: Frequent Pattern Generation in Data Mining with Python Implementation In this article, an advanced method called the FP Growth …
fpgrowth: Frequent itemsets via the FP-growth algorithm
In particular, and what makes it different from the Apriori frequent pattern mining algorithm, FP-Growth is an frequent pattern mining algorithm that does not require candidate generation.
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FP-Growth — FP-Growth 1.0 documentation - Read the Docs
FP-Growth ¶ A Python implementation of the Frequent Pattern Growth algorithm. Free software: ISC license Documentation: https://fp-growth.readthedocs.org.
FPGrowth.ipynb - Colab
In the first part, we describe the basic approach to find frequent patterns in a transactional database using the FP-growth algorithm. In the final part, we …
pyfpgrowth · PyPI
Apr 27, 2016 · FP-Growth A Python implementation of the Frequent Pattern Growth algorithm. Free software: ISC license Documentation: https://fp-growth.readthedocs.org. Getting Started You can …
FP Growth: Frequent Pattern Generation in Data Mining …
Oct 30, 2020 · In this article, an advanced method called the FP Growth algorithm will be revealed. We will walk through the whole process of the FP Growth …
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