Research Topic
Research Topic: Improved Apriori Algorithm
Section titled “Research Topic: Improved Apriori Algorithm”Background
Section titled “Background”The Apriori algorithm is a classic algorithm for mining frequent itemsets in transactional databases. It uses a bottom-up approach, generating candidate itemsets and then checking their frequency against the database. The FP-Growth algorithm, introduced by Han et al. in 2000, offers an alternative approach that avoids candidate generation by using a tree-based data structure.
Problem Statement
Section titled “Problem Statement”Traditional Apriori algorithms face several challenges:
- Multiple database scans: Requires scanning the database multiple times
- Large candidate sets: Generates many candidate itemsets that may not be frequent
- Memory overhead: Stores all candidate itemsets in memory
- Scalability issues: Performance degrades with large datasets
While FP-Growth addresses many of these issues, understanding both algorithms and their trade-offs is crucial for:
- Selecting the appropriate algorithm for different scenarios
- Developing improved variants
- Understanding the theoretical foundations of frequent itemset mining
Methodology
Section titled “Methodology”The research will involve:
- Literature review of existing improvements to Apriori
- Analysis of FP-Growth algorithm and its advantages
- Algorithm design and analysis for improved Apriori
- Implementation of both Apriori (improved) and FP-Growth algorithms
- Experimental evaluation on benchmark datasets
- Performance comparison between improved Apriori and FP-Growth
- Analysis of trade-offs and use cases for each algorithm