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Incremental high average-utility itemset mining: survey and challenges.

Jing Chen1,2, Shengyi Yang3, Weiping Ding4

  • 1School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, China.

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|April 30, 2024
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Summary
This summary is machine-generated.

This paper reviews incremental High Average Utility Itemset Mining (iHAUIM) algorithms for dynamic databases. These methods efficiently update high average utility itemsets without re-processing the entire dataset.

Keywords:
Dynamic data miningHigh Average Utility Item MiningHigh Utility Item MiningPattern mining

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Area of Science:

  • Computer Science
  • Data Mining
  • Database Systems

Background:

  • Traditional High Average Utility Itemset Mining (HAUIM) algorithms are designed for static datasets.
  • Real-world applications require dynamic databases with frequent transaction updates.
  • Incremental HAUIM (iHAUIM) algorithms address the need for efficient updates in evolving databases.

Purpose of the Study:

  • To provide a comprehensive review of state-of-the-art incremental High Average Utility Itemset Mining (iHAUIM) algorithms.
  • To analyze the unique characteristics, advantages, and disadvantages of various iHAUIM techniques.
  • To explore future research directions and extensions in dynamic HAUIM.

Main Methods:

  • Explanation of iHAUIM concepts with formulas and real-world examples.
  • Categorization of iHAUIM algorithms into Apriori-based, Tree-based, and Utility-list-based techniques.
  • Critical analysis of the strengths and weaknesses of different iHAUIM mining approaches.

Main Results:

  • iHAUIM algorithms offer significant cost reduction for discovering high average utility itemsets in dynamic databases.
  • Different algorithmic approaches (Apriori-based, Tree-based, Utility-list-based) present distinct trade-offs in performance and complexity.
  • The review synthesizes current knowledge on iHAUIM, highlighting its practical relevance.

Conclusions:

  • Incremental approaches are crucial for efficient HAUIM in dynamic environments.
  • Further research can explore novel extensions and optimizations for iHAUIM algorithms.
  • The findings support the adoption of iHAUIM for applications requiring real-time data analysis.