2006
DOI: 10.1016/j.neunet.2006.05.013
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Advanced visualization of Self-Organizing Maps with vector fields

Abstract: Self-Organizing Maps have been applied in various industrial applications and have proven to be a valuable data mining tool. In order to fully benefit from their potential, advanced visualization techniques assist the user in analyzing and interpreting the maps. We propose two new methods for depicting the SOM based on vector fields, namely the Gradient Field and Borderline visualization techniques, to show the clustering structure at various levels of detail. We explain how this method can be used on aggregat… Show more

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Cited by 38 publications
(22 citation statements)
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References 22 publications
(20 reference statements)
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“…Further analyses are now easier because the amount of data is reduced and patterns in the data are more evident. In an exploratory data-analysis, the interpretation of SOM is regularly based on illustrations, where quantitative information is depicted as a colour values on the map lattice (Pölzlbauer et al, 2006). Those maps, called component planes, reveal the distribution of each variable of original data on the SOM.…”
Section: > Statistical Analyses and Modellingmentioning
confidence: 99%
See 1 more Smart Citation
“…Further analyses are now easier because the amount of data is reduced and patterns in the data are more evident. In an exploratory data-analysis, the interpretation of SOM is regularly based on illustrations, where quantitative information is depicted as a colour values on the map lattice (Pölzlbauer et al, 2006). Those maps, called component planes, reveal the distribution of each variable of original data on the SOM.…”
Section: > Statistical Analyses and Modellingmentioning
confidence: 99%
“…The SOM is an unsupervised artificial neural network especially feasible in an exploratory data analysis. The SOM can be used as a data visualization tool that performs a non-linear projection from a high-dimensional feature space onto a twodimensional map lattice (Pölzlbauer et al, 2006). In practice, large multivariable data can be presented to the user as intuitive pictures i.e.…”
Section: Introductionmentioning
confidence: 99%
“…Also, shape-based and vector-based SOM cluster visualizations have been introduced. 9,19 Further approaches using color to distinguish between clusters exist. 17 In addition, this cluster visualizations enable detecting topographic errors on the map, as SOM prototypes with similar vector attributes are colored with similar color, accordingly.…”
Section: Som Visualization and Applicationsmentioning
confidence: 99%
“…The U-Matrix [7] shows local cluster boundaries by depicting pair-wise distances of neighbouring model vectors. The Gradient Field [4] has some similarity with the U-Matrix, but applies smoothing over a broader neighbourhood. It uses a vector field style of representation, where each arrow points to its closest cluster centre.…”
Section: Self-organising Map and Visualisationsmentioning
confidence: 99%