Big Multidimensional Datasets Visualization Using Neural Networks – Efficient Decision Support

  • Dzemyda G
  • Medvedev V
  • Lupeikiene A
  • et al.
N/ACitations
Citations of this article
6Readers
Mendeley users who have this article in their library.

Abstract

Pages 1-11 Published online by RTU Press, https://csimq-journals.rtu.lv http://dx. Abstract. Nowadays business information systems are thought of as decision-oriented systems supported by different types of subsystems. Multidimensional data visualization is an essential part of such systems. As datasets tend to be increasingly large, more effective ways are required to display, analyze and interpret information they contain. Most of the classical visualization methods are unsuitable for large datasets. This paper focuses on the artificial neural networks-based methods for visualization of big multidimensional datasets; namely, on the approaches for the faster obtaining of visual results. The new strategy, which is identified by the decreased number of cycles of data reviews (passes of training data) up to the only one, when training neural networks, is proposed. To test this strategy, the results of experiments, using two unsupervised learning methods on benchmark data, are briefly presented.

Cite

CITATION STYLE

APA

Dzemyda, G., Medvedev, V., Lupeikiene, A., Kurasova, O., & Caplinskas, A. (2016). Big Multidimensional Datasets Visualization Using Neural Networks – Efficient Decision Support. Complex Systems Informatics and Modeling Quarterly, (6), 1–11. https://doi.org/10.7250/csimq.2016-6.01

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free