Research Overview
Project Information
Section titled “Project Information”Course: COS-781 (Data Mining)
Research Topic: Improved Apriori Algorithm and FP-Growth Algorithm
Focus: Efficient frequent itemset mining
Objectives
Section titled “Objectives”This research project aims to:
- Analyze the limitations of the traditional Apriori algorithm
- Study the FP-Growth algorithm and its advantages
- Propose and implement improvements to enhance Apriori performance
- Compare improved Apriori with FP-Growth algorithm
- Evaluate both algorithms against standard benchmarks
- Document findings and present results
Motivation
Section titled “Motivation”The Apriori algorithm is a fundamental algorithm in association rule mining, but it has known limitations in terms of efficiency, especially with large datasets. FP-Growth provides an alternative approach that avoids candidate generation. This research explores methods to improve Apriori’s performance while maintaining correctness, and compares it with FP-Growth to understand the trade-offs between different approaches.
Expected Outcomes
Section titled “Expected Outcomes”- A comprehensive analysis of the Apriori and FP-Growth algorithms
- An improved version of Apriori with documented enhancements
- Comparative analysis between improved Apriori and FP-Growth
- Experimental results comparing performance
- Documentation and presentation materials