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Comparison of Two Dimension-Reduction Methods for Network Simulation Models.

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  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899-0001.

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This study explores dimension reduction techniques for network simulation data. Principal components analysis and correlation with clustering effectively identify key behaviors in complex datasets.

Keywords:
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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Analysis

Background:

  • Simulation models for data communication networks generate complex, multivariate data.
  • This data often contains redundancies, obscuring underlying network behaviors.
  • Effective analysis requires reducing data dimensionality to focus on significant patterns.

Purpose of the Study:

  • To investigate and compare two dimension reduction methods for multivariate data from network simulation models.
  • To identify key network behaviors by simplifying high-dimensional datasets.
  • To provide insights for analysts dealing with complex simulation outputs.

Main Methods:

  • Correlation analysis combined with clustering.
  • Principal Components Analysis (PCA).
  • Application of both methods to a 22-dimensional dataset from a network simulator.

Main Results:

  • Both methods successfully reduced the dimensionality of the network simulation data.
  • The study identified key issues for analysts to consider when choosing a method.
  • A comparison of the dimension reduction effectiveness of the two approaches was performed.

Conclusions:

  • Dimension reduction techniques are valuable for understanding complex network simulation data.
  • Principal Components Analysis and correlation with clustering are effective methods for this purpose.
  • These methods can potentially be applied to empirical data from real networks.