Introduction
Research Motivation
- Analyze limitations of traditional Apriori algorithm
- Study FP-Growth algorithm advantages
- Propose improvements to Apriori performance
- Compare improved Apriori with FP-Growth
Problem Statement
Traditional Apriori algorithms face challenges:
- Multiple database scans
- Large candidate sets
- Memory overhead
- Scalability issues
Background
Apriori Algorithm
- Classic algorithm for frequent itemset mining
- Bottom-up approach with candidate generation
- Uses Apriori property for pruning
FP-Growth Algorithm
- Tree-based approach avoiding candidate generation
- Requires only two database scans
- More efficient for dense datasets
Methodology
Proposed Improvements
- Reduced database scans
- Optimized candidate generation
- Memory optimization
- Performance enhancements
Implementation Approach
- Traditional Apriori (baseline)
- Improved Apriori (test)
- FP-Growth (comparison)
Results
- Execution time: Improved Apriori vs FP-Growth
- Memory usage: Analysis across algorithms
- Scalability: Performance with varying dataset sizes
Key Findings
[Results will be documented here]
Conclusion
Summary of Contributions
- Novel improvements to Apriori algorithm
- Comprehensive performance analysis
- Comparative evaluation with FP-Growth
- Open-source implementation
Future Work
- Further optimizations
- Hybrid approaches
- Extended evaluation