Exploratory Data Analytics for Information Discovery in a Network Structure

Abstract

This report presents an analytic strategy for visual exploration of multidimensional data. Node position in a network structure is determined by projecting from the high-dimensional data (HDD) space to a low-dimensional latent space. Clustering of node position vectors may result for making inferences. Dimensionality reduction by feature extraction of HDD for visualization is performed using a parametric Student's t-distribution for stochastic neighbor embedding (t-SNE). The resultant t-SNE network of nodes for a Euclidean space can now be examined using visual analytics technology-navigation/interaction within the visualization of the data. Scene content is described using the Extensible 3-D (X3D) graphics application programming interface. The immersive profile of an X3D scene allows for navigation within the data for possible information discovery. Such an approach may provide for a better understanding of data and facilitate analytical reasoning that would otherwise be difficult in an exclusively textual context.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2011
Accession Number
ADA556725

Entities

People

  • Andrew M. Neiderer

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Application Programming Interface
  • Computer Programming
  • Data Analysis
  • Data Visualization
  • Dimensionality Reduction
  • Embedding
  • Extraction
  • Feature Extraction
  • Geometry
  • Graphics
  • Information Science
  • Language
  • Military Research
  • Three Dimensional
  • Two Dimensional
  • Visualizations
  • Web Browsers

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Vision.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • Space