TY - CONF
T1 - SIRIUS: Dual, Symmetric, Interactive Dimension Reductions
T2 - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST)
Y1 - 2018
A1 - Michelle Dowling
A1 - Wenskovitch, John
A1 - J.T. Fry
A1 - Leman, Scotland
A1 - House, Leanna
A1 - North, Chris
KW - attribute projection
KW - dimension reduction
KW - exploratory data analysis
KW - observation projection
KW - Semantic interaction
AB - Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.
JF - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST)
ER -
TY - RPRT
T1 - Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills
Y1 - 2015
A1 - Self, Jessica Zeitz
A1 - Self, Nathan
A1 - House, Leanna
A1 - Jane Robertson Evia
A1 - Leman, Scotland
A1 - North, Chris
KW - dimension reduction
KW - education
KW - multidimensional scaling
KW - multivariate analysis
KW - Visual Analytics
AB - This paper addresses the use of visual analytics in education for teaching what we call cognitive dimensionality (CD) and other EDA skills. We present the concept of CD to characterize students' capacity for making dimensionally complex insights from data. Using this concept, we build a vocabulary and methodology to support a studentâ€™s progression in terms of growth from low cognitive dimensionality (LCD) to high cognitive dimensionality (HCD). Crucially, students do not need high-level math skills to develop HCD. Rather, we use our own tool called Andromeda that enables human-computer interaction with a common, easy to interpret visualization method called Weighted Multidimensional Scaling (WMDS) to promote the idea of making high-dimensional insights. In this paper, we present Andromeda and report findings from a series of classroom assignments to 18 graduate students. These assignments progress from spreadsheet manipulations to statistical software such as R and finally to the use of Andromeda. In parallel with the assignments, we saw students' CD begin low and improve.
PB - Virginia Tech
CY - Blacksburg
ER -