The experimental study of the effectiveness of Kohonen maps and autoassociative neural networks in the qualitative analy
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ORIGINAL ARTICLE
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 Dariusz Jamro´z1 Received: 10 July 2018 / Accepted: 20 March 2020 The Author(s) 2020
Abstract 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 Multidimensional data analysis Data mining Self-organized neural network Autoassociative neural network Kohonen maps
1 Introduction Methods utilizing neural networks for analyzing multidimensional data through their visualization are widely used in practice [1–5]. Visualization of multidimensional data consists in using the representation transforming a multidimensional space into a two-dimensional space representing a computer screen. This representation should preserve properties of these data crucial for the conducted & Dariusz Jamro´z [email protected] 1
Department of Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krako´w, Poland
analysis. Neural networks are well suited for different kinds of representations [6–9], so they can also be used for this type of representation. 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
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