Socio-economic Data Analysis with Scan Statistics and Self-organizing Maps

EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-directed embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.

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Self-organizing maps and entropy applied to data analysis of functional magnetic resonance images

Applied Mathematical Sciences ◽

10.12988/ams.2014.310585 ◽

2014 ◽

Vol 8 ◽

pp. 4953-4969

Author(s):

Anderson D. S. Campelo ◽

Valcir J. C. Farias ◽

Heliton R. Tavares ◽

Marcus P. da C. da Rocha

Keyword(s):

Data Analysis ◽

Magnetic Resonance ◽

Magnetic Resonance Images ◽

Functional Magnetic Resonance ◽

Self Organizing Maps ◽

Self Organizing

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Issues in Using Self-Organizing Maps in Human Movement and Sport Science

International Journal of Computer Science in Sport ◽

10.1515/ijcss-2017-0001 ◽

2017 ◽

Vol 16 (1) ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

B. Serrien ◽

M. Goossens ◽

J-P. Baeyens

Keyword(s):

Data Analysis ◽

Statistical Tests ◽

Research Question ◽

Human Movement ◽

Original Data ◽

Sensitivity Analyses ◽

Self Organizing Maps ◽

Sport Science ◽

Related Data ◽

Self Organizing

AbstractSelf-Organizing Maps (SOMs) are steadily more integrated as data-analysis tools in human movement and sport science. One of the issues limiting researchers’ confidence in their applications and conclusions concerns the (arbitrary) selection of training parameters, their effect on the quality of the SOM and the sensitivity of any subsequent analyses. In this paper, we demonstrate how quality and sensitivity may be examined to increase the validity of SOM-based data-analysis. For this purpose, we use two related data sets where the research question concerns coordination variability in a volleyball spike. SOMs are an attractive tool for analysing this problem because of their ability to reduce the highdimensional time series to a two-dimensional problem while preserving the topological, non-linear relations in the original data. In a first step, we systematically search the SOM parameter space for a set of options that produces significantly lower continuity, accuracy and combined map errors and we discuss the sensitivity of SOM-based analyses of coordination variability to changes in training parameters. In a second step, we further investigate the effect of using different numbers of trials and variables on the SOM quality and sensitivity. These sensitivity analyses are able to validate the conclusions from statistical tests. Using this type of analysis can guide researchers to select SOM parameters that optimally represent their data and to examine how they affect the subsequent analyses. This may also enforce confidence in any conclusions that are drawn from studies using SOMs and enhance their integration in human movement and sport science.

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