Jessica Zeitz Self, Michelle Dowling, John Wenskovitch, Scotland C. Leman, Chris North


Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multidimensional scaling, support data exploration by projecting datasets to two dimensions for visualization. These projections can be explored through parametric interaction, tweaking underlying parameterizations, and observation-level interaction, directly interacting with the points within the projection. In this article, we present the results of a controlled usability study determining the differences, advantages, and drawbacks among parametric interaction, observation-level interaction, and their combination. The study assesses both interaction technique effects on domain-specific high-dimensional data analyses performed by non-experts of statistical algorithms. This study is performed using Andromeda, a tool that enables both parametric and observation-level interaction to provide in-depth data exploration. The results indicate that the two forms of interaction serve different, but complementary, purposes in gaining insight through steerable dimension reduction algorithms.


Chris North

John Wenskovitch

Scotland C. Leman

Publication Details

Date of publication:
June 13, 2018
ACM Transactions on Interactive Intelligent Systems
Page number(s):
Issue Number:
Publication note:

Jessica Zeitz Self, Michelle Dowling, John E. Wenskovitch, Ian Crandell, Ming Wang, Leanna House, Scotland Leman, Chris North: Observation-Level and Parametric Interaction for High-Dimensional Data Analysis. ACM Trans. Interact. Intell. Syst. 8(2): 15:1-15:36 (2018)