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A novel association rule mining approach using TID intermediate itemset.

Iyad Aqra1, Tutut Herawan1, Norjihan Abdul Ghani1

  • 1Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

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Summary
This summary is machine-generated.

This study introduces a novel association rule mining (ARM) approach that efficiently updates thresholds without rescanning databases. This method significantly reduces computation costs for real-time data mining applications.

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Area of Science:

  • Data Mining
  • Database Systems
  • Artificial Intelligence

Background:

  • Association Rule Mining (ARM) is crucial for knowledge discovery in transactional databases.
  • Current ARM algorithms struggle with dynamic threshold adjustments, requiring costly database rescans.
  • This inefficiency limits real-time applications and scalability.

Purpose of the Study:

  • To develop an efficient ARM algorithm for multilevel transactional databases.
  • To address the challenge of dynamic threshold updates without full database rescans.
  • To enhance the feasibility of ARM for real-time applications.

Main Methods:

  • A novel ARM approach creating intermediate itemsets.
  • Application of thresholds to extract categorical frequent itemsets with diverse values.
  • Utilizing itemset intersection to determine real support without database rescans.

Main Results:

  • The proposed method avoids rescanning the entire database for threshold updates.
  • Achieved significant improvements in efficiency and reduced computation costs.
  • Demonstrated capability for parallel deployment, enhancing real-time association rule discovery.

Conclusions:

  • The novel ARM approach offers a more efficient solution for dynamic threshold adjustments.
  • Outperforms existing methods in terms of computation cost, accuracy, and completeness.
  • Suitable for real-world deployments and real-time data mining scenarios.