When exploring large, high-dimensional datasets, analysts often utilize two techniques for reducing the data to make exploration more tractable. The first technique, dimension reduction, reduces the high-dimensional dataset into a low-dimensional space while preserving high-dimensional structures. The second, clustering, groups similar observations while simultaneously separating dissimilar observations. Existing work presents a number of systems and approaches that utilize these techniques; however, these techniques can cooperate or conflict in unexpected ways.
The core contribution of this work is the systematic examination of the design space at the intersection of dimension reduction and clustering when building intelligent, interactive tools in visual analytics. I survey existing techniques for dimension reduction and clustering algorithms in visual analytics tools, and I explore the design space for creating projections and interactions that include dimension reduction and clustering algorithms in the same visual interface. Further, I implement and evaluate three prototype tools that implement specific points within this design space. Finally, I run a cognitive study to understand how analysts perform dimension reduction (spatialization) and clustering (grouping) operations. Contributions of this work include surveys of existing techniques, three interactive tools and usage cases demonstrating their utility, design decisions for implementing future tools, and a presentation of complex human organizational behaviors. / Doctor of Philosophy / When an analyst is exploring a dataset, they seek to gain insight from the data. With data sets growing larger, analysts require techniques to help them reduce the size of the data while still maintaining its meaning. Two commonly-utilized techniques are dimension reduction and clustering. Dimension reduction seeks to eliminate unnecessary features from the data, reducing the number of columns to a smaller number. Clustering seeks to group similar objects together, reducing the number of rows to a smaller number. The contribution of this work is to explore how dimension reduction and clustering are currently being used in interactive visual analytics systems, as well as to explore how they could be used to address challenges faced by analysts in the future. To do so, I survey existing techniques and explore the design space for creating visualizations that incorporate both types of computations. I look at methods by which an analyst could interact with those projections in other to communicate their interests to the system, thereby producing visualizations that better match the needs of the analyst. I develop and evaluate three tools that incorporate both dimension reduction and clustering in separate computational pipelines. Finally, I conduct a cognitive study to better understand how users think about these operations, in order to create guidelines for better systems in the future.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/96599 |
| Date | 06 September 2019 |
| Creators | Wenskovitch Jr, John Edward |
| Contributors | Computer Science, North, Christopher L., Leman, Scotland C., Bowman, Douglas A., Polys, Nicholas F., Marai, G. Elisabeta |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf, video/mp4, text/vtt, video/mp4, text/vtt, video/mp4, text/vtt |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0018 seconds