Visual Analytics for Understanding Multiple Attributes

Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay and Stefan Wrobel
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Natalia Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Gennady Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Georg Fuchs: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Aidan Slingsby: City, University of London, Northampton Square, Department of Computer Science
Cagatay Turkay: University of Warwick, Centre for Interdisciplinary Methodologies
Stefan Wrobel: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven

Chapter Chapter 6 in Visual Analytics for Data Scientists, 2020, pp 181-200 from Springer

Abstract: Abstract One very common challenge that every data scientists has to deal with is to make sense of data sets with many attributes, where “many” can sometimes be tens, sometimes hundreds, and even thousands. Whether your goal is to do exploratory analysis on the relationships between the attributes, or to build models of the underlying phenomena, working with many dimensions is not trivial. The high number of attributes is a barrier against using some of the standard visual representations: just try to imagine a scatterplot matrix where you want to look at the pairwise distributions combinations of 100 variables. Moreover, any computational method that you apply produces results that are challenging to interpret. Even linear regression, one of the easiest models to understand, becomes quite complex if you need to investigate the interactions between hundreds of variables. This chapter will discuss how not to get lost in these high-dimensional spaces and how visual analytics techniques can help you navigate your way through.

Date: 2020
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DOI: 10.1007/978-3-030-56146-8_6

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