Adaptive resonance theory (ART)
The handbook of brain theory and neural networks
Introduction to Neural and Cognitive Modeling
Introduction to Neural and Cognitive Modeling
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
A motion compression/reconstruction method based on max t-norm composite fuzzy relational equations
Information Sciences: an International Journal
Failure prediction of dotcom companies using neural network-genetic programming hybrids
Information Sciences: an International Journal
On an ant colony-based approach for business fraud detection
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Behavioral assessment of recoverable credit of retailer's customers
Information Sciences: an International Journal
A case study on financial ratios via cross-graph quasi-bicliques
Information Sciences: an International Journal
Business intelligence for delinquency risk management via cox regression
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
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With the constantly changing and deceptive strategies that can be concealed in complex of financial statements, traditional means of financial analysis is unable to detect these accounting frauds in advance. In order to detect new accounting frauds and find out the true meaning of off-balance sheet arrangements, we propose an easy and feasible method using an unsupervised learning system. In unsupervised learning, the training of the network is entirely data-driven and no target results are provided. The features that do not help in clustering can be removed. With unsupervised learning it is possible to learn larger and more complex relations than with supervised learning. In the demonstration, we extract four non-traditional warning signals using adaptive resonance theory, with Enron and WorldCom as prototypes to identify the possibility of potential fraud of a company that investors or analysts may be concerned with.