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NetNCSP: Nonoverlapping closed sequential pattern mining.

Youxi Wu1,2,3, Changrui Zhu1, Yan Li4

  • 1School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.

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|April 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Nettree for Nonoverlapping Closed Sequential Pattern (NetNCSP), an efficient algorithm for sequential pattern mining. NetNCSP reduces redundant patterns and improves performance, particularly in biological sequence analysis.

Keywords:
COVID-19Closed pattern miningNettreeNonoverlapping sequence patternPeriodic wildcard gapsSequential pattern mining

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Area of Science:

  • Computer Science
  • Bioinformatics

Background:

  • Traditional sequential pattern mining (SPM) often overlooks pattern repetition.
  • Gap constraint and nonoverlapping SPM address limitations but can still yield redundant patterns.

Purpose of the Study:

  • To propose a complete algorithm, NetNCSP, for nonoverlapping closed sequential pattern mining.
  • To enhance efficiency and reduce pattern redundancy in SPM.

Main Methods:

  • Developed NetNCSP algorithm utilizing the Nettree structure.
  • Implemented backtracking for efficient nonoverlapping support calculation.
  • Introduced three pruning strategies: inheriting, predicting, and determining.

Main Results:

  • NetNCSP demonstrates superior efficiency and discovers more compressible closed patterns.
  • Experimental results confirm reduced redundancy and improved mining performance.
  • Application to SARS-CoV-2 and SARS viruses revealed similar pattern compositions.

Conclusions:

  • NetNCSP offers an effective solution for nonoverlapping closed sequential pattern mining.
  • The algorithm provides valuable insights into viral sequence patterns.
  • NetNCSP balances efficiency, completeness, and pattern compressibility.