Adapting Self-Organizing Map Algorithm to Sparse Data

Melka, Josué; Mariage, Jean-Jacques

doi:10.1007/978-3-030-16469-0_8

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Additionally, these methods can reflect data category density, that is, less frequently occurring items in data are represented by smaller clusters and those occurring more frequentlyby larger clusters. Thanks to such an ability to maintain the topology, visualization methods utilizing neural networks, and in particular modifications of these methods specially created for the analysis of this type of data, are perfect also for visual analysis of sparse datasets [33][34][35][36][37][38]. Sparse data are characterized by that although vectors occurring in such data have very large dimensions, only a small percent of coordinates of these vectors is nonzero.…”

Section: Possibilities Of Visualizations Based On Autoassociative Neumentioning

confidence: 99%

The experimental study of the effectiveness of Kohonen maps and autoassociative neural networks in the qualitative analysis of multidimensional data by the example of real data describing coal susceptibility to fluidal gasification

Jamroz

2020

Neural Comput & Applic

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The qualitative analysis of multidimensional data using their visualization allows to observe some characteristics of data in a way which is the most natural for a human, through the sense of sight. Thanks to such an approach, some characteristics of the analyzed data are simply visible. This allows to avoid using often complex algorithms allowing to examine specific data properties. Visualization of multidimensional data consists in using the representation transforming a multidimensional space into a two-dimensional space representing a computer screen. The important information which can be obtained in this way is the possibility to separate points belonging to different classes in the multidimensional space. Such information can be directly obtained if images of points belonging to different classes occupy other areas of the picture presenting these data. The paper presents the effectiveness of the qualitative analysis of multidimensional data conducted in this way through their visualization with the application of Kohonen maps and autoassociative neural networks. The obtained results were compared with results obtained using the perspective-based observational tunnels method, PCA, multidimensional scaling and relevance maps. Effectiveness tests of the above methods were performed using real sevendimensional data describing coal samples in terms of their susceptibility to fluidal gasification. The methods' effectiveness was compared using the criterion for the readability of the multidimensional visualization results, introduced in earlier papers. Keywords Multidimensional visualization Á Multidimensional data analysis Á Data mining Á Self-organized neural network Á Autoassociative neural network Á Kohonen maps & Dariusz Jamróz

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Section: Possibilities Of Visualizations Based On Autoassociative Neumentioning

confidence: 99%