FP-Growth Algorithm

FP-Growth Algorithm Overview

The FP-Growth (Frequent Pattern Growth) algorithm is an efficient alternative to the Apriori algorithm for mining frequent itemsets. It was proposed by Han et al. in 2000 and uses a tree-based data structure to compress the database representation.

Algorithm Steps

1. Scan database: Count support for each item and identify frequent 1-itemsets
2. Sort items: Order items by frequency (descending) and remove infrequent items
3. Build FP-Tree: Construct the FP-tree by inserting transactions one by one
4. Mine FP-Tree: Recursively mine the FP-tree to find all frequent itemsets
   • For each item in the header table (from least frequent to most):
     • Generate conditional pattern base
     • Construct conditional FP-tree
     • Recursively mine the conditional FP-tree

FP-Tree Construction

The FP-tree is built by:

1. Sorting items in each transaction by frequency
2. Inserting transactions as paths in the tree
3. Sharing common prefixes between transactions
4. Maintaining header tables for efficient traversal

Experimental Results

Our implementation demonstrates FP-Growth’s superior performance compared to Apriori. See the Results page for detailed performance analysis and comparison.

Relationship to Improved Apriori

While FP-Growth offers a fundamentally different approach, understanding both algorithms helps in:

• Comparing performance characteristics
• Understanding trade-offs between approaches
• Developing hybrid methods
• Applying concepts from FP-Growth to improve Apriori-based algorithms