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Related Experiment Videos

Knowledge discovery in high-dimensional data: case studies and a user survey for the rank-by-feature framework.

Jinwook Seo1, Ben Shneiderman

  • 1Children's Research Institute, Washington, DC 20010, USA. jseo@cnmcresearch.org

IEEE Transactions on Visualization and Computer Graphics
|April 28, 2006
PubMed
Summary

The Hierarchical Clustering Explorer (HCE) aids high-dimensional data analysis. Evaluation showed HCE is beneficial, but improved training methods are needed for users to master its novel features.

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Area of Science:

  • Computer Science
  • Data Science
  • Bioinformatics

Background:

  • High-dimensional data analysis presents significant challenges.
  • Visual analytic tools offer powerful solutions but require user adaptation.
  • The Hierarchical Clustering Explorer (HCE) was developed to address these challenges.

Purpose of the Study:

  • To evaluate the utility and user skill acquisition of the Hierarchical Clustering Explorer (HCE).
  • To assess the effectiveness of the rank-by-feature framework within HCE.
  • To gather user feedback for refining HCE's capabilities and training.

Main Methods:

  • Development of interactive visual analytic tools, including the Hierarchical Clustering Explorer (HCE).
  • Incorporation of the rank-by-feature framework for multivariate data exploration.

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  • Evaluation through three case studies and an email user survey (n=57) focusing on skill acquisition.

    • Main Results:

    • User survey confirmed the benefits of HCE for knowledge discovery in high-dimensional data.
    • Case studies and surveys provided insights into HCE's strengths and weaknesses.
    • Both evaluations indicated a need for enhanced user training methods.

    Conclusions:

    • The Hierarchical Clustering Explorer (HCE) is a valuable tool for exploring complex datasets.
    • User adoption and proficiency can be improved with better training materials.
    • Further development should consider user feedback for enhanced usability.