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Identification of failing banks using clustering with self-organising neural networks
Citation
Negnevitsky, M, Identification of failing banks using clustering with self-organising neural networks, Procedia Computer Science, 12-14 June 2017, Zurich, Switzerland, pp. 1327-1333. ISSN 1877-0509 (2017) [Conference Extract]
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Copyright Statement
Copyright 2017 The Authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/
DOI: doi:10.1016/j.procs.2017.05.125
Abstract
This paper presents experimental results of cluster analysis using self organising neural networks for identifying failing banks. The paper first describes major reasons and likelihoods of bank failures. Then it demonstrates an application of a self-organising neural network and presents results of the study. Findings of the paper demonstrate that a self-organising neural network is a powerful tool for identifying potentially failing banks. Finally, the paper discusses some of the limitations of cluster analysis related to understanding of the exact meaning of each cluster.
Item Details
| Item Type: | Conference Extract |
|---|---|
| Keywords: | cluster analysis, self-organising neural network, kohonen layer |
| Research Division: | Engineering |
| Research Group: | Control engineering, mechatronics and robotics |
| Research Field: | Field robotics |
| Objective Division: | Manufacturing |
| Objective Group: | Machinery and equipment |
| Objective Field: | Machinery and equipment not elsewhere classified |
| UTAS Author: | Negnevitsky, M (Professor Michael Negnevitsky) |
| ID Code: | 129718 |
| Year Published: | 2017 |
| Web of Science® Times Cited: | 4 |
| Deposited By: | Engineering |
| Deposited On: | 2018-12-17 |
| Last Modified: | 2018-12-17 |
| Downloads: | 120 View Download Statistics |
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