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FIRE: a two-level interactive visualization for deep exploration of association rules

  • Abhishek MukherjiEmail author
  • Xika Lin
  • Ermal Toto
  • Christopher R. Botaish
  • Jason Whitehouse
  • Elke A. Rundensteiner
  • Matthew O. Ward
Regular Paper
  • 89 Downloads

Abstract

While rule mining is critical for decision-making applications, rule mining systems still lack support for interactive exploration of multitude of generated rules and understanding of relationships among rule results produced with various parameter settings. Based on a novel parameter space-driven approach, our proposed Framework forInteractiveRuleExploration [FIRE (PARAS/FIRE homepage: http://paras.cs.wpi.edu/)] addresses this usability shortcoming. FIRE features innovative visual displays and interactions to enable interactive rule exploration. We propose two linked interactive displays, namely the parameter space view (PSpace) and the rule space view (RSpace) that together enable enhanced sense-making of rule relationships. The PSpace view visualizes the distribution of rules produced for diverse parameter settings. This not only facilitates user parameter selection for rule mining but also enhances an analyst’s understanding of rule relationships in the parameter space context. The RSpace view provides a detailed display of the rules using a novel rule glyph visualization to facilitate interactive visual rule comparisons. We evaluate the usability and effectiveness of our FIRE framework with two studies. First, in a case study a researcher explored a dataset of interest using the FIRE paradigm as well as the state-of-the-art rule visualization techniques from the ARulsViz R package. Further, our user study with 22 subjects establishes the usability and effectiveness of the proposed visual displays and interactions of FIRE using several benchmark datasets. Overall, this research encompasses significant contributions at the intersection of data mining and visual analytics.

Keywords

Interactive data discovery Rule space exploration Data visualization Rule mining 

Notes

Acknowledgements

This work was supported by NSF under Grants IIS-0812027, CCF-0811510 and IIS-1117139.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Abhishek Mukherji
    • 1
    Email author
  • Xika Lin
    • 2
  • Ermal Toto
    • 2
  • Christopher R. Botaish
    • 2
  • Jason Whitehouse
    • 2
  • Elke A. Rundensteiner
    • 2
  • Matthew O. Ward
    • 2
  1. 1.Cisco Systems Inc.San JoseUSA
  2. 2.Department of Computer ScienceWorcester Polytechnic InstituteWorcesterUSA

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