Quality Based Guidance for Exploratory Dimensionality Reduction
2013 (English)In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 12, no 1, 44-64 p.Article in journal (Refereed) Published
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.
High-dimensional data, dimensionality reduction, quality metrics, visual exploration, interactive visual analysis
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-70859DOI: 10.1177/1473871612460526ISI: 000315073700003OAI: oai:DiVA.org:liu-70859DiVA: diva2:442159
Funding Agencies|Unilever Discover Port Sunlight||Swedish Research Council in the Linnaeus Centre CADICS||Visualization Programme||2011-09-202011-09-202013-03-21Bibliographically approved