Experience with a System for Manual Clustering of Air Surveillance Track Data

Abstract

Using clustering algorithms to mine air surveillance track data for groups of similar flights has the potential to facilitate a variety of capability enhancements. Since there are many algorithms that could be used, a method for assessing the quality of algorithm output is required. One potential method is to have a human expert hand-craft a clustering for a test data set, and use this manual clustering as the gold-standard against which the output of a clustering algorithm is assessed. For complex spatio-temporal data such as air surveillance track data, the manual construction of clusterings for a robust test data suite will be labour-intensive and reliant on good information technology support. This report describes an experimental system providing a user interface and workflow for performing manual clustering of air surveillance track data, and experience with a trial of the system.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2013
Accession Number
ADA595833

Entities

People

  • Matthew C. Lowry

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Intelligence
  • Anomaly Detection
  • Australia
  • Change Detection
  • Construction
  • Data Mining
  • Data Sets
  • Detection
  • Efficiency
  • Flight
  • Flight Paths
  • Graphical User Interface
  • Information Systems
  • Situational Awareness
  • Standards
  • Surveillance
  • User Interface

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Computer Vision.
  • Distributed Systems and Data Platform Development