liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
A Task Based Performance Evaluation of Visualization Approaches for Categorical Data Analysis.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
2011 (English)In: Proceedings - 15th International Conferenceon Information Visualisation, Los Alamitos, CA, USA: IEEE Computer Society, 2011, 80-89 p.Conference paper (Other academic)
Abstract [en]

Categorical data is common within many areas and efficient methods for analysis are needed. It is, however, often difficult to analyse categorical data since no general measure of similarity exists. One approach is to represent the categories with numerical values (quantification) prior to visualization using methods for numerical data. Another is to use visual representations specifically designed for categorical data. Although commonly used, very little guidance is available as to which method may be most useful for different analysis tasks. This paper presents an evaluation comparing the performance of employing quantification prior to visualization and visualization using a method designed for categorical data. It also provides a guidance as to which visualization approach is most useful in the context of two basic data analysis tasks: one related to similarity structures and one related to category frequency. The results strongly indicate that the quantification approach is most efficient for the similarity related task, whereas the visual representation designed for categorical data is most efficient for the task related to category frequency.

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2011. 80-89 p.
Keyword [en]
Categorical Data, Quantitative Evaluation, Usability Studies, Parallel Sets, Quantification
National Category
Media Engineering
URN: urn:nbn:se:liu:diva-70855DOI: 10.1109/IV.2011.92ISBN: 978-1-4577-0868-8OAI: diva2:442133
15th International Conference on Information Visualisation (IV), 2011 , 13-15 July, London, UK
Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2011-12-05
In thesis
1. Algorithmically Guided Information Visualization: Explorative Approaches for High Dimensional, Mixed and Categorical Data
Open this publication in new window or tab >>Algorithmically Guided Information Visualization: Explorative Approaches for High Dimensional, Mixed and Categorical Data
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Algoritmiskt vägledd informationsvisualisering för högdimensionell och kategorisk data
Abstract [en]

Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex.

This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables.

The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis.

Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 72 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1400
Information visualization, data mining, high dimensional data, categorical data, mixed data
National Category
Computer Science
urn:nbn:se:liu:diva-70860 (URN)978-91-7393-056-7 (ISBN)
Public defence
2011-11-11, Domen, Norrköpings Visualiseringscenter, Kungsgatan 54, 602 33 Norrköping, 09:15 (English)
Available from: 2011-10-06 Created: 2011-09-20 Last updated: 2011-12-05Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Johansson Fernstad, SaraJohansson, Jimmy
By organisation
Media and Information TechnologyThe Institute of Technology
Media Engineering

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 149 hits
ReferencesLink to record
Permanent link

Direct link