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Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules.

Thang Mai1, Loan T T Nguyen2, Bay Vo3

  • 1Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.

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|February 22, 2020
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Summary
This summary is machine-generated.

This study introduces an efficient method for mining non-redundant high-utility association rules (NR-HARs), crucial for business strategy and decision-making. The approach accelerates data analysis and reduces memory usage for better insights.

Keywords:
Internet of Thingsdata mininghigh-utility association rulehigh-utility itemsetlatticenon-redundant high-utility association rule

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Area of Science:

  • Data Mining
  • Business Intelligence
  • Decision Support Systems

Background:

  • Managers utilize product association information for strategic planning.
  • High-utility association rules (HARs) aid decision-making by incorporating user-defined weights.
  • Existing HAR mining methods may generate redundant rules, limiting efficiency in some systems.

Purpose of the Study:

  • To propose an efficient method for mining non-redundant high-utility association rules (NR-HARs).
  • To develop an algorithm that generates a minimal set of HARs for practical decision-support systems.

Main Methods:

  • Building a semi-lattice of high-utility itemsets.
  • Identifying closed and generator itemsets within the lattice.
  • Developing an efficient algorithm for generating rules from the lattice structure.

Main Results:

  • The proposed NR-HARs algorithm demonstrates faster runtime compared to existing methods.
  • The algorithm requires comparable or less memory than traditional approaches.
  • Verification on diverse datasets confirms the efficiency and effectiveness of the method.

Conclusions:

  • The NR-HARs method provides an efficient way to extract essential association rules for business intelligence.
  • The algorithm's compatibility with systems like the Internet of Things (IoT) and distributed computing enhances its applicability.
  • This approach supports timely decision-making by visualizing customer needs and optimizing business strategies.