Skip to main content
Top
Published in: Neural Computing and Applications 9/2021

21-11-2020 | S.I.: SPIoT 2020

Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules

Authors: Hongyu Shang, Duan Lu, Qingyuan Zhou

Published in: Neural Computing and Applications | Issue 9/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

As the big data, Internet of Things, cloud computing, and other ideas and technologies are integrated into social life, the big data technology can improve the corporate financial data processing. At the same time, with the fiercer competition between enterprises, investors and enterprises have paid more attention to the role of financial crisis warning in corporate management. The work selected the multiple financial indicators based on big data mining in Internet of Things. The rules between all financial indicators were found to choose more representative financial risk indicators. Then the frequent fuzzy option set was determined by FCM (fuzzy cluster method), parallel rules, and parallel mining algorithm, thus obtaining the fuzzy association rules that satisfy the minimum fuzzy credibility. Finally, the relevant data of listed companies were selected to analyze the corporate financial risks, which verified the method proposed in the work.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Fitzpatrick PJ (1932) A comparison of ratios of successful industrial enterprises with those of failed firms [J]. Account Publishing Computer 2:589–605 Fitzpatrick PJ (1932) A comparison of ratios of successful industrial enterprises with those of failed firms [J]. Account Publishing Computer 2:589–605
2.
go back to reference Altman EI (1968) Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy [J]. J Finance 23(4):589–609CrossRef Altman EI (1968) Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy [J]. J Finance 23(4):589–609CrossRef
3.
go back to reference Meyer PA, Pifer HW (1970) Prediction of bank failures [J]. J Finance 25(4):853–868CrossRef Meyer PA, Pifer HW (1970) Prediction of bank failures [J]. J Finance 25(4):853–868CrossRef
4.
go back to reference Laitimen EK (1993) Financial predictors for different phases of the failure process [J]. Omega 21(2):215–228CrossRef Laitimen EK (1993) Financial predictors for different phases of the failure process [J]. Omega 21(2):215–228CrossRef
5.
go back to reference Zhu HY, Yong G (2002) The Designing Processing of a Crisis Early Warning System of Core Competence Strategy [J]. Soft Sci 16(5):13–16 Zhu HY, Yong G (2002) The Designing Processing of a Crisis Early Warning System of Core Competence Strategy [J]. Soft Sci 16(5):13–16
6.
go back to reference Ren H, Xu XS (2003) A warning system of enterprise’s crisis management [J]. J WUT (Inf Manage Eng) 25(06):153–157 Ren H, Xu XS (2003) A warning system of enterprise’s crisis management [J]. J WUT (Inf Manage Eng) 25(06):153–157
7.
go back to reference Wu YY, Cai QP, Wu F (2008) Pre-warning study of corporations’ financial crisis based on ANN technique [J]. J Southeast Univ (Philos Soc Sci) 10(1):22–26 Wu YY, Cai QP, Wu F (2008) Pre-warning study of corporations’ financial crisis based on ANN technique [J]. J Southeast Univ (Philos Soc Sci) 10(1):22–26
8.
go back to reference Yang GJ, Zhou YM, Sun LL (2009) Enterprise financial early warning method based on Benford-logistic model [J]. J Quantitative Tech Econ 10:149–165 Yang GJ, Zhou YM, Sun LL (2009) Enterprise financial early warning method based on Benford-logistic model [J]. J Quantitative Tech Econ 10:149–165
9.
go back to reference Sivapathasekaran C, Mukherjee S, Ray A, Gupta A, Sen R (2010) Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant [J]. Biores Technol 101(8):2884–2887CrossRef Sivapathasekaran C, Mukherjee S, Ray A, Gupta A, Sen R (2010) Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant [J]. Biores Technol 101(8):2884–2887CrossRef
10.
go back to reference Koyuncugil AS, Ozgulba N (2012) Financial early waiving system model and data mining application for risk detection [J]. Expert Sys Appl 39(6):62–65CrossRef Koyuncugil AS, Ozgulba N (2012) Financial early waiving system model and data mining application for risk detection [J]. Expert Sys Appl 39(6):62–65CrossRef
11.
go back to reference Banerjee A et al (2014) Data analytics: hyped up aspirations or true potential [J]. Vikalpa J Decis Mak 38(4):1–11 Banerjee A et al (2014) Data analytics: hyped up aspirations or true potential [J]. Vikalpa J Decis Mak 38(4):1–11
12.
go back to reference Cao Y (2012) Aggregating multiple classification results using Choquet integral for financial distress early warning [J]. Expert Sys Appl 39(2):112–123CrossRef Cao Y (2012) Aggregating multiple classification results using Choquet integral for financial distress early warning [J]. Expert Sys Appl 39(2):112–123CrossRef
15.
go back to reference Maimon O, Rokach L (2009) Introduction to Knowledge Discovery and Data Mining. In: Maimon O, Rokach L (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MAMATH Maimon O, Rokach L (2009) Introduction to Knowledge Discovery and Data Mining. In: Maimon O, Rokach L (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MAMATH
16.
go back to reference Song B, Zhu JM, Li X (2015) The research of enterprise financial early warning based on big data [J]. J Cent Univ Finance Econ 06:55–64 Song B, Zhu JM, Li X (2015) The research of enterprise financial early warning based on big data [J]. J Cent Univ Finance Econ 06:55–64
19.
21.
go back to reference Bodon F. (2003) A fast APRIORI implementation. In: Proceedings of the ICDM workshop on frequent itemset mining implementations (FIMI ’03), Melbourne, Florida, USA. Bodon F. (2003) A fast APRIORI implementation. In: Proceedings of the ICDM workshop on frequent itemset mining implementations (FIMI ’03), Melbourne, Florida, USA.
22.
go back to reference Agrawal R, Srikant R. (1994) Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB. pp. 487–499. Agrawal R, Srikant R. (1994) Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB. pp. 487–499.
23.
go back to reference Babuška R, Verbruggen H (2003) Neuro-fuzzy methods for nonlinear system identification. Annual Rev Control 27(1):73–85CrossRef Babuška R, Verbruggen H (2003) Neuro-fuzzy methods for nonlinear system identification. Annual Rev Control 27(1):73–85CrossRef
27.
go back to reference Lin F, Liang D, Chen E (2011) Financial ratio selection for business crisis prediction. Expert Syst Appl 38(12):15094–15102CrossRef Lin F, Liang D, Chen E (2011) Financial ratio selection for business crisis prediction. Expert Syst Appl 38(12):15094–15102CrossRef
Metadata
Title
Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules
Authors
Hongyu Shang
Duan Lu
Qingyuan Zhou
Publication date
21-11-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 9/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05510-5

Other articles of this Issue 9/2021

Neural Computing and Applications 9/2021 Go to the issue

Premium Partner