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NetNMSP: Nonoverlapping maximal sequential pattern mining.

Yan Li1, Shuai Zhang2, Lei Guo3

  • 1School of Economics and Management, Hebei University of Technology, Tianjin, 300401 China.

Applied Intelligence (Dordrecht, Netherlands)
|January 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces NetNMSP for Nonoverlapping Maximal Sequential Pattern (NMSP) mining, reducing redundant patterns and improving efficiency. NMSP mining offers better data compression and reveals distinct viral sequence differences.

Keywords:
Backtracking strategyCOVID-19Gap constraintMERS-CoVMaximal pattern miningNonoverlapping pattern miningSequential pattern mining

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Area of Science:

  • Data Mining
  • Bioinformatics
  • Machine Learning

Background:

  • Traditional sequential pattern mining generates numerous redundant patterns, hindering efficiency and information extraction.
  • Nonoverlapping Maximal Sequential Pattern (NMSP) mining aims to identify frequent patterns whose super-patterns are infrequent, reducing redundancy.
  • Existing methods struggle with efficiency and retaining expressive power when dealing with large datasets.

Purpose of the Study:

  • To propose an effective algorithm, NetNMSP, for Nonoverlapping Maximal Sequential Pattern (NMSP) mining.
  • To enhance the efficiency and reduce the number of mined sequential patterns.
  • To demonstrate the utility of NMSP mining in biological sequence analysis and large-scale data scalability.

Main Methods:

  • NetNMSP algorithm utilizes a backtracking strategy with a leftmost parent node method in a Nettree for efficient support calculation.
  • Candidate patterns are generated using a pattern join strategy to minimize the search space.
  • A screening method is employed to determine the final Nonoverlapping Maximal Sequential Patterns (NMSPs).

Main Results:

  • NetNMSP outperforms existing state-of-the-art algorithms on biological sequence datasets.
  • NMSP mining demonstrates superior compression performance compared to closed pattern mining.
  • The algorithm exhibits excellent scalability on large-scale sales datasets.
  • Analysis of SARS-CoV-1, SARS-CoV-2, and MERS-CoV reveals similarities in short viral patterns and differences in long patterns, with NMSP mining effectively highlighting these distinctions.

Conclusions:

  • NetNMSP is an effective algorithm for NMSP mining, offering improved efficiency and pattern reduction.
  • NMSP mining provides a valuable approach for data compression and identifying subtle differences in sequential data, particularly in viral genomics.
  • The proposed method shows strong scalability and practical applicability for analyzing large datasets and complex biological sequences.