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The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree.
1Jiaozuo Normal College, Jiaozuo 454000, China.
Computational Intelligence and Neuroscience
|May 31, 2022
Summary
This study introduces FP-MFIA, an efficient maximum frequent itemset mining algorithm. FP-MFIA improves mining speed and reduces storage space by using a one-way FP-tree and MFI-list.
Area of Science:
- Data Mining
- Database Systems
Background:
- Association rule mining is crucial for discovering relationships in database attributes.
- Mining maximum frequent itemsets is a key challenge, with some applications requiring only these specific patterns.
Purpose of the Study:
- To introduce FP-MFIA, a novel algorithm for efficient maximum frequent itemset mining.
- To improve upon existing methods by optimizing both mining speed and memory usage.
Main Methods:
- FP-MFIA utilizes a one-way FP-tree structure, enabling data mining with only two scans.
- A redefined MFI-list data structure efficiently stores maximum frequent itemsets and releases unnecessary nodes.
Main Results:
- The FP-MFIA algorithm demonstrates significantly improved mining efficiency compared to IDMFIA and DMFIA.
- The approach effectively reduces the space required for storing maximum frequent itemsets.
Conclusions:
- FP-MFIA offers a practical and efficient solution for maximum frequent itemset mining.
- The algorithm's performance enhancements make it suitable for data mining applications with specific needs.
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