期刊
ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS
卷 8, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3158230
关键词
Usability; user interface; visual analytics; dimension reduction; interaction; evaluation; data analysis
资金
- National Science Foundation [IIS-1447416, IIS-1218346, DUE-1141096]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1447416] Funding Source: National Science Foundation
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.
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