A guide to expert systems
Capabilities of outlier detection schemes in large datasets, framework and methodologies
Knowledge and Information Systems
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
Relational data pre-processing techniques for improved securities fraud detection
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Credit Card Fraud Detection Using Hidden Markov Model
IEEE Transactions on Dependable and Secure Computing
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concepts for novelty detection and handling based on a case-based reasoning process scheme
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Establishing fraud detection patterns based on signatures
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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We propose a simple and efficient method to detect exceptional data, which includes a novel end user explanation facility. After various designs, the best was based on an unsupervised learning schema, which uses an adaptation of the artificial neural network paradigm ART for the cluster task. In our method, the cluster that contains the smaller number of instances is considered as outlier data. The method provides an explanation to the end user about why this cluster is exceptional with regard to the data universe. The proposed method has been tested and compared successfully not only with well-known academic data, but also with a real and very large financial database that contains attributes with numerical and categorical values.