Authors:
Ronak Etemadpour
1
;
Robson Carlos da Motta
2
;
Jose Gustavo de Souza Paiva
3
;
Rosane Minghim
2
;
Maria Cristina Ferreira de Oliveira
2
and
Lars Linsen
1
Affiliations:
1
Jacobs University Bremen, Germany
;
2
Universidade de São Paulo, Brazil
;
3
Federal University of Uberlândia, Brazil
Keyword(s):
Projections, Multidimensional Data, Perception-based Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
High-Dimensional Data and Dimensionality Reduction
;
Interpretation and Evaluation Methods
;
Perception and Cognition in Visualization
Abstract:
Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve
similarity relations, multidimensional projections or other dimension reduction techniques are commonly used
to project high-dimensional data point to a 2D point using a certain strategy for the 2D layout.Typical analysis
tasks for projected multidimensional data do not necessarily match the expectations of human perception.
Learning more about the effectiveness of projection layouts from a users perspective is an important step
towards consolidating their role in supporting visual analytics tasks. Those tasks often involve detecting and
correlating clusters. To understand the role of orientation and cluster properties of size, shape and density, we
first conducted a study with synthetic 2D scatter plots, where we can set the respective properties manually.
Then we picked five projection methods representative of different approaches to generate layouts of high
dimensional da
ta for two domains, image and document data. The users were asked to identify the clusters
on real-world data and answers to questions were compared for correctness against ground truth computed
directly from the data. Our results offer interesting insight on the use of projection layouts in data visualization
tasks.
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