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Toward a Methodology for Agent-Based Data Mining and Visualization

  • Elizabeth Sklar
  • Chipp Jansen
  • Jonathan Chan
  • Michael Byrd
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7103)

Abstract

We explore the notion of agent-based data mining and visualization as a means for exploring large, multi-dimensional data sets. In Reynolds’ classic flocking algorithm (1987), individuals move in a 2-dimensional space and emulate the behavior of a flock of birds (or “boids”, as Reynolds refers to them). Each individual in the simulated flock exhibits specific behaviors that dictate how it moves and how it interacts with other boids in its “neighborhood”. We are interested in using this approach as a way of visualizing large multi-dimensional data sets. In particular, we are focused on data sets in which records contain time-tagged information about people (e.g., a student in an educational data set or a patient in a medical records data set). We present a system in which individuals in the data set are represented as agents, or “data boids”. The flocking exhibited by our boids is driven not by observation and emulation of creatures in nature, but rather by features inherent in the data set. The visualization quickly shows separation of data boids into clusters, where members are attracted to each other by common feature values.

Keywords

Geographic Information System Cellular Automaton Categorical Feature Information Visualization Steering Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Elizabeth Sklar
    • 1
    • 2
  • Chipp Jansen
    • 1
    • 3
  • Jonathan Chan
    • 1
  • Michael Byrd
    • 2
  1. 1.Brooklyn CollegeThe City University of New YorkUSA
  2. 2.The Graduate CenterThe City University of New YorkUSA
  3. 3.Hunter CollegeThe City University of New YorkUSA

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