Spotlighting Anomalies using Frequent Patterns
Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, PMLR 71:33-42, 2018.
Approaches for anomaly detection based on frequent pattern mining follow the paradigm: if an instance contains more frequent patterns, it means that this data instance is unlikely to be an anomaly. This concept can be used in financial industry to reveal contextual anomalies. The main contribution of this paper is an approach that includes a novel formula for computation of anomaly scores. We evaluated the proposed approach on baseline datasets and present a use case on a real world financial dataset. We also propose a way how to explain the anomaly to the users. Implementations of the evaluated algorithms and experiments are available online in R.