A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications
Li P, Niggemann O, Hammer B (2018)
In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE: 5345-5352.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Autor*in
Li, Peng;
Niggemann, Oliver;
Hammer, BarbaraUniBi 
Abstract / Bemerkung
Recent clustering based anomaly detection technologies classify new observations in different ways, e.g. using probability distributions, cluster centers or whole data points. Some of which suffer from high false classification rate, while others require high computational resources. In this paper, we propose a geometric approach to clustering based anomaly detection, in which the boundaries of clusters are utilized to classify new observations instead. To identify the cluster boundaries, a new algorithm for generating n-dimensional non-convex hulls has been developed. The proposed approach can improve the accuracy of clustering based anomaly detection, meanwhile, doesn't need high computational resources. Furthermore, it is universally applicable for any kind of cluster algorithms. The effectiveness of this approach is evaluated with real world data collected from different industrial automation systems.
Erscheinungsjahr
2018
Titel des Konferenzbandes
IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
Seite(n)
5345-5352
Konferenz
IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
Konferenzort
Washington, DC, USA
eISBN
978-1-5090-6684-1
Page URI
https://pub.uni-bielefeld.de/record/2982086
Zitieren
Li P, Niggemann O, Hammer B. A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE; 2018: 5345-5352.
Li, P., Niggemann, O., & Hammer, B. (2018). A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 5345-5352. IEEE. https://doi.org/10.1109/IECON.2018.8592906
Li, Peng, Niggemann, Oliver, and Hammer, Barbara. 2018. “A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications”. In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 5345-5352. IEEE.
Li, P., Niggemann, O., and Hammer, B. (2018). “A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications” in IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society (IEEE), 5345-5352.
Li, P., Niggemann, O., & Hammer, B., 2018. A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, pp. 5345-5352.
P. Li, O. Niggemann, and B. Hammer, “A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications”, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2018, pp.5345-5352.
Li, P., Niggemann, O., Hammer, B.: A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. p. 5345-5352. IEEE (2018).
Li, Peng, Niggemann, Oliver, and Hammer, Barbara. “A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications”. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2018. 5345-5352.
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