DimRedPlot: A Generic Visualisation Tool for Dimensionality Reduced Data

Winter, K. L. (2016) DimRedPlot: A Generic Visualisation Tool for Dimensionality Reduced Data. Master's Thesis / Essay, Computing Science.

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Abstract

Dimensionality reduction techniques can transform datasets with a large number of variables to simpler two or three-dimensional datasets, while preserving distances and structure in the original data as much as possible. This makes these techniques very useful when dealing with large datasets. Unfortunately, the results they produce can be abstract, making it hard to fully understand how these results relate to the original data. As a result, many researchers treat these techniques as simple black boxes, which means they severely underutilise their potential. Most of them also are only capable of either analysing numerical or categorical data, which makes analysing mixed datasets a difficult challenge. This thesis presents DimRedPlot, a tool which, when combined with more general visualisation techniques, allows users to easily see the relation between the results of linear dimensionality reduction techniques and their original data. The focus on linear techniques, such as Principal Component Analysis, is due to the fact that they have been widely used for decades in a wide range of applications. Because of the support of both Principal Component Analysis, capable of analysing numerical data, (Multiple) Correspondence Analysis, capable of analysing categorical data, and the ability to combine these analyses on one screen, DimRedPlot greatly simplifies working with mixed datasets. DimRedPlot has been designed and evaluated at the Luxembourg Institute of Science and Technology, or LIST, and it has been integrated into the larger RParcoords environment developed there. The evaluation was performed using two datasets generated and used at the institute, and DimRedPlot continues to be used by researchers at the LIST.

Item Type:	Thesis (Master's Thesis / Essay)
Degree programme:	Computing Science
Thesis type:	Master's Thesis / Essay
Language:	English
Date Deposited:	15 Feb 2018 08:10
Last Modified:	15 Feb 2018 08:10
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/13638

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