CCE Theses and Dissertations
Date of Award
2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science (CISD)
Department
College of Computing and Engineering
Advisor
Michael Laszlo
Committee Member
Francisco Mitropoulous
Committee Member
Sumitra Mukerjee
Keywords
ANOVA, Categorical, Chart, Data, Multivariate, Visualization
Abstract
Multivariate Categorical Data (MCD) plays a significant role in many industries, and the ability to understand the data is critical for insight and decision making. Visualization is a key tool for understanding the data. This dissertation designed and implemented a novel technique for visualizing MCD called Pivoting Parallel Charts (PPC). The design of PPC was informed by studying several existing MCD visualization techniques.
PPC visualizes MCD as a sequence of parallel axes with affixed bar charts. A user-specified axis, called the pivot, acts as the crucial point of consideration for all data relationships. The bar charts are color-coded by the categories of the pivot, enabling users to probe the relationship between multiple dimensions even when their axes are non-contiguous in the current ordering of axes. PPC standard mode is used to reveal relationships between the pivot and each of the other axes, and paired mode between the pivot and successive pairs of dimensions.
A user study involving IT professionals, some of whom attend college, was used to assess the new technique's effectiveness in comparison to the Sunburst and Parallel Sets visualization techniques. PPC outperformed Sunburst and Parallel Sets in terms of accuracy while task completion times and error rates for dimension adjacency showed no statistically significant difference among techniques. Future work involves testing the effectiveness and scalability of PPC on larger multivariate categorical datasets as well as developing enhancements to the PPC implementation.
NSUWorks Citation
Janice James. 2024. A Technique for Visualization of Multivariate Categorical Data. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Computing and Engineering. (1202)
https://nsuworks.nova.edu/gscis_etd/1202.
