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Visual Analysis of Mixed Data Sets Using Interactive Quantification
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
2009 (English)In: ACM SIGKDD Explorations Newsletter, ISSN 1931-0145, Vol. 11, no 2, 29-38 p.Article in journal (Refereed) Published
Abstract [en]

It is often diffcult to analyse data sets including a combi-nation of categorical and numerical variables (mixed datasets) since there does not exist any similarity measure whichis as straight forward and general as the numerical distancebetween numerical items. Quantication of categorical vari-ables enables analysis using commonly used visual represen-tations and analysis techniques for numerical data. Thispaper presents a tool for exploratory analysis of categoricaland mixed data which uses a quantication process intro-duced in [Johansson2008]. The application enables analysis of mixeddata sets by providing an environment for exploratory anal-ysis using common visual representations in multiple coordi-nated views and algorithmic analysis that facilitates detec-tion of potentially interesting patterns within combinationsof categorical and numerical variables. The generality andusefulness of the quantication process and of the featuresof the application is demonstrated through a case scenariousing a data set from the IEEE VAST 2008 Challenge.

Place, publisher, year, edition, pages
New York: ACM , 2009. Vol. 11, no 2, 29-38 p.
Keyword [en]
Information Visualization, Visual Analysis, Categorical Data, Quantification
National Category
Media Engineering
URN: urn:nbn:se:liu:diva-60142DOI: 10.1145/1809400.1809406OAI: diva2:355304
Available from: 2010-10-06 Created: 2010-10-06 Last updated: 2011-10-06
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

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