Evaluation of clustering algorithms for financial risk analysis using MCDM methods

By Gang Kou, Yi Peng, Guoxun Wang. Information Sciences, 2014

708 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f270bb4c167ca5151f73012f0e0bfb42866aebfc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David Martínez-Rego
2. Diego Raphael Amancio
3. Luciano da Fontoura Costa
4. Odemir Martinez Bruno
5. Carmine Ventre
6. Leslie Kanthan
7. Lingbo Li
8. Mayra Zegarra Rodriguez

Citations in the corpus

• Clustering algorithms: A comparative approach (2016, methodology): “In [43], the authors propose an evaluation approach based in a multiple criteria decision making in the domain of financial risk analysis over three real world credit risk and bankruptcy risk datasets.”
• Cryptocurrency trading: a comprehensive survey (2020, background): “Automated detection clusters are critical as they help to understand sub-patterns of data that can be used to infer user behaviour and identify potential risks (Li et al. 2021; Kou et al. 2014).”