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The Mining Algorithm of Maximum Frequent Itemsets Based on Frequent Pattern Tree

Summary

Researchers developed a new computer algorithm called FP-MFIA that efficiently finds patterns in large databases. This algorithm is faster and uses less computer memory than similar tools by using a special tree structure and storing information more efficiently. Tests on different types of databases showed this new method works better than existing alternatives.

Background

Association rule mining is a fundamental topic in data mining that discovers relationships between database attributes. Maximum frequent itemsets contain information about all frequent itemsets and are important for mining association rules, as some applications only require mining maximum frequent itemsets.

Objective

This research introduces FP-MFIA, a new maximum frequent itemset mining algorithm based on FP-tree structure. The study aims to develop an efficient algorithm that requires less memory, fewer operations, and faster execution compared to existing algorithms like IDMFIA and DMFIA.

Results

Experimental testing on Mushroom and Connect datasets shows that FP-MFIA is more efficient than IDMFIA and DMFIA algorithms under both high and low support conditions. The algorithm demonstrates superior performance especially for sparsely distributed databases and long-pattern frequent itemset mining.

Conclusion

The FP-MFIA algorithm successfully improves mining efficiency of intensive data through optimized storage structures and reduced tree traversals. While the algorithm shows clear performance advantages, limitations remain as data scale increases and candidate itemsets multiply, requiring further research development.
  • Published in:Computational Intelligence and Neuroscience,
  • Study Type:Algorithm Development and Comparative Analysis,
  • Source: 10.1155/2022/7022168, PMID: 35634074
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