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Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism.

Ya-Han Hu1, Yen-Liang Chen1

  • 1Department of Information Management, National Central University, Chung-Li 320, Taiwan, ROC.

Decision Support Systems
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces efficient algorithms for mining association rules with multiple minimum supports, improving upon existing methods. The new algorithms, CFP-growth and an MIS-tree maintenance approach, enhance scalability and performance in data mining.

Keywords:
Association rulesData miningFP-treeMinimum supports

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Area of Science:

  • Computer Science
  • Data Mining
  • Database Systems

Background:

  • Traditional association rule mining uses a single minimum support threshold.
  • Generalizing to multiple minimum supports allows for more nuanced pattern discovery.
  • Existing methods like MSapriori have limitations in efficiency and scalability.

Purpose of the Study:

  • To develop more efficient and scalable algorithms for mining association rules with multiple minimum supports.
  • To introduce a novel FP-tree-like structure (MIS-tree) for storing frequent pattern information.
  • To propose an efficient algorithm for tuning item supports without rescanning the database.

Main Methods:

  • Development of the CFP-growth algorithm, an MIS-tree-based approach for mining frequent itemsets.
  • Design of an algorithm to maintain the MIS-tree structure efficiently during threshold tuning.
  • Comparative experiments using synthetic and real-life datasets.

Main Results:

  • The proposed CFP-growth algorithm demonstrates significant efficiency gains over the MSapriori algorithm.
  • The MIS-tree structure effectively stores crucial information for pattern mining.
  • The database-rescan-free tuning algorithm accelerates the process of finding satisfactory item supports.

Conclusions:

  • The novel algorithms offer substantial improvements in efficiency and scalability for multiple minimum support association rule mining.
  • The MIS-tree and associated algorithms provide a powerful framework for complex data mining tasks.
  • This work facilitates more practical and efficient application of association rule mining in diverse datasets.