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IPHM: Incremental periodic high-utility mining algorithm in dynamic and evolving data environments.

Huiwu Huang1, Shixi Chen1, Jiahui Chen1

  • 1Guangdong University of Technology, School of Computer Science and Technology, Guangzhou, 510006, China.

Heliyon
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces the Incremental Periodic High-Utility Itemset Miner (IPHM) for efficient pattern discovery in dynamic market basket data. The IPHM algorithm effectively identifies recurring profitable purchase behaviors in evolving datasets.

Keywords:
High-utility itemsetIncremental miningPattern miningPeriodic itemset

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Area of Science:

  • Data Mining
  • Database Systems
  • Machine Learning

Background:

  • Traditional high-utility itemset mining struggles with dynamic, incremental databases.
  • Periodic high-utility itemset (PHUI) mining identifies recurring profitable patterns but is computationally expensive with data updates.
  • Existing algorithms require full dataset reprocessing for new transactions, leading to inefficiency.

Purpose of the Study:

  • To develop an efficient algorithm for mining periodic high-utility itemsets in incremental database environments.
  • To address the limitations of static database assumptions in PHUI mining.
  • To enable real-time analysis of evolving customer purchase behaviors.

Main Methods:

  • Introduction of the Incremental Periodic High-Utility Itemset Miner (IPHM) algorithm.
  • Development of an innovative incremental utility-list structure optimized for dynamic data.
  • Implementation of effective pruning strategies to accelerate list construction and candidate discarding.

Main Results:

  • The IPHM algorithm demonstrates high efficacy and efficiency in incremental database scenarios.
  • Experimental results confirm the algorithm's ability to adapt quickly to database changes.
  • The method significantly reduces the computational cost associated with updating PHUI mining.

Conclusions:

  • IPHM provides an efficient solution for discovering periodic high-utility itemsets in dynamic market basket databases.
  • The algorithm's adaptability makes it highly suitable for real-world applications with frequent data updates.
  • This research enhances the practical applicability of PHUI mining in evolving data environments.