Using clustering ensemble to identify banking business models
Author:
Affiliation:
1. Universidade Católica Portuguesa, Católica Porto Business School Porto Portugal.
2. Universidade do Porto, Faculdade de Economia, CEF.UP, Porto Portugal
Funder
Fundação para a Ciência e a Tecnologia
Publisher
Wiley
Subject
Finance,General Business, Management and Accounting
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/isaf.1471
Reference94 articles.
1. A comparative study on base classifiers in ensemble methods for credit scoring
2. The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study
3. A new hybrid ensemble credit scoring model based on classifiers consensus system approach
4. Aryuni M. Madyatmadja E. D. &Miranda E.(2018).Customer segmentation in XYZ bank usingK‐means andK‐medoids clustering. Proceedings of 2018 International Conference on Information Management and Technology (ICIMTech) 412–416.
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