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Quality Based Guidance for Exploratory Dimensionality Reduction
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Unilever Discover Port Sunlight, UK.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
2013 (English)In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 12, no 1, 44-64 p.Article in journal (Refereed) Published
Abstract [en]

High dimensional data sets containing hundreds of variables are difficult to explore, since traditional visualization methods often are unable to represent such data effectively. Dimensionality reduction is commonly employed prior to visualization to address this difficulty, and numerous dimensionality reduction methods are available. However, few dimensionality reduction approaches take the importance of several structures into account and few provide an overview of structures existing in the full high dimensional data set. For exploratory analysis, as well as for many other tasks, several structures may be of interest and exploration of the full high dimensional data set without reduction may also be desirable.This paper presents methods for exploratory analysis and interactive dimensionality reduction, where automated methods are employed to analyse and rank the variables using a range of quality metrics, providing one or more measures of ‘interestingness’ for individual variables. Through ranking, a single value of interestingness is obtained based on several quality metrics which is usable as a threshold for the most interesting variables. An interactive environment is presented where the user is provided many possibilities to explore and gain understanding of the structures within the high dimensional data set, all based on quality metrics and ranking. Guided by this, the analyst can explore the high dimensional data set and select interactively a subset of the potentially most interesting variables, employing various interactive methods for dimensionality reduction. The effectiveness and usefulness of the system is demonstrated through a use-case analysing data from a DNA sequence-based study of bacterial populations.

Place, publisher, year, edition, pages
Palgrave Macmillan / SAGE Publications (UK and US) , 2013. Vol. 12, no 1, 44-64 p.
Keyword [en]
High-dimensional data, dimensionality reduction, quality metrics, visual exploration, interactive visual analysis
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-70859DOI: 10.1177/1473871612460526ISI: 000315073700003OAI: diva2:442159

Funding Agencies|Unilever Discover Port Sunlight||Swedish Research Council in the Linnaeus Centre CADICS||Visualization Programme||

Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2013-03-21Bibliographically approved
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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