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The Analysis of the Effectiveness of the Perspective-Based Observational Tunnels Method by the Example of the Evaluation of Possibilities to Divide the Multidimensional Space of Coal Samples

  • Dariusz Jamroz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10862)

Abstract

Methods of qualitative analysis of multidimensional data using visualization of this data consist in using the transformation of a multidimensional space into a two-dimensional one. In this way, multidimensional complicated data can be presented on a two-dimensional computer screen. This allows to conduct the qualitative analysis of this data in a way which is the most natural for people, through the sense of sight. The application of complex algorithms targeted to search for multidimensional data of specific properties can be replaced with such a qualitative analysis. Some qualitative characteristics are simply visible in the two-dimensional image representing this data. The new perspective-based observational tunnels method is an example of the multidimensional data visualization method. This method was used in this paper to present and analyze the real set of seven-dimensional data describing coal samples obtained from two hard coal mines. This paper presents for the first time the application of perspective-based observational tunnels method for the evaluation of possibilities to divide the multidimensional space of coal samples by their susceptibility to fluidal gasification. This was performed in order to verify whether it will be possible to indicate the possibility of such a division by applying this method. Views presenting the analyzed data, enabling to indicate the possibility to separate areas of the multidimensional space occupied by samples with different applicability for the gasification process, were obtained as a result.

Keywords

Multidimensional data analysis Data mining Multidimensional visualization Observational tunnels method Multidimensional perspective Fluidal gasification 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied Computer ScienceAGH University of Science and TechnologyKrakowPoland

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