Spotlighting Anomalies using Frequent Patterns

Jaroslav Kuchar, Vojtech Svatek
Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, PMLR 71:33-42, 2018.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v71-kuchar18a, title = {Spotlighting Anomalies using Frequent Patterns}, author = {Kuchar, Jaroslav and Svatek, Vojtech}, booktitle = {Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance}, pages = {33--42}, year = {2018}, editor = {Anandakrishnan, Archana and Kumar, Senthil and Statnikov, Alexander and Faruquie, Tanveer and Xu, Di}, volume = {71}, series = {Proceedings of Machine Learning Research}, month = {14 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v71/kuchar18a/kuchar18a.pdf}, url = {https://proceedings.mlr.press/v71/kuchar18a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Spotlighting Anomalies using Frequent Patterns %A Jaroslav Kuchar %A Vojtech Svatek %B Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance %C Proceedings of Machine Learning Research %D 2018 %E Archana Anandakrishnan %E Senthil Kumar %E Alexander Statnikov %E Tanveer Faruquie %E Di Xu %F pmlr-v71-kuchar18a %I PMLR %P 33--42 %U https://proceedings.mlr.press/v71/kuchar18a.html %V 71 %X 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.
APA
Kuchar, J. & Svatek, V.. (2018). Spotlighting Anomalies using Frequent Patterns. Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, in Proceedings of Machine Learning Research 71:33-42 Available from https://proceedings.mlr.press/v71/kuchar18a.html.

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