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Mining high occupancy patterns to analyze incremental data in intelligent systems.

Heonho Kim1, Taewoong Ryu1, Chanhee Lee1

  • 1Department of Computer Engineering, Sejong University, Seoul, Republic of Korea.

ISA Transactions
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces HOMI, a novel approach for high occupancy pattern mining in real-time incremental databases. HOMI efficiently detects patterns, outperforming existing methods in speed and memory usage.

Keywords:
Data miningHigh occupancy patternIncremental databasePattern mining

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Area of Science:

  • Data Mining
  • Database Systems
  • Artificial Intelligence

Background:

  • High occupancy pattern mining offers improved frequent pattern discovery.
  • Current methods struggle with real-time incremental databases and high memory demands.
  • Advancements in IT necessitate adaptable data mining techniques.

Purpose of the Study:

  • To develop an efficient algorithm for high occupancy pattern mining on incremental databases.
  • To address the limitations of existing state-of-the-art approaches.
  • To enable automated decision-making in dynamic environments.

Main Methods:

  • Proposed HOMI (High Occupancy pattern Mining on Incremental databases) algorithm.
  • Utilized a Depth-First Search (DFS)-based approach for pattern detection.
  • Evaluated performance on both real-world and synthetic datasets.

Main Results:

  • HOMI effectively mines high occupancy patterns in incremental databases.
  • Demonstrated superior performance compared to state-of-the-art and related algorithms.
  • HOMI shows improved efficiency in terms of speed and memory usage.

Conclusions:

  • HOMI provides an effective solution for high occupancy pattern mining in dynamic databases.
  • The DFS-based approach enhances scalability and reduces memory footprint.
  • This research facilitates advanced automated control systems through efficient pattern discovery.