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Loners Stand Out. Identification of Anomalous Subsequences Based on Group Performance

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12447)


Time series analysis is a part of data mining and nowadays an important field of research due to the increasing amount of data that is recorded sequentially by various systems. Especially the identification of anomalous subsequences arouses great interest, since a manual search for errors or malfunctions is not possible in most cases. Often outliers are defined as points or sequences that deviate significantly from the course of one or multiple time series, yet there are also applications where the trend rather than the exact course of time series is relevant. In that case, there is an approach of clustering the time series per time point and analyzing their cluster transitions over time. Sequences that change their cluster members suddenly or often, indicate an anomaly.

In 2019, a novel approach for the detection of these transition-based outliers was introduced [19]. Now, we present an algorithm called DACT (Detecting Anomalies based on Cluster Transitions) that is able to identify outlier sequences of the same type. It is a simple approach that stands out due to different results, although a similar type of anomalies is targeted. In the evaluation, we examine and discuss the differences. Our experiments show, that the results are competitive and reasonable.


  • Outlier detection
  • Time series analysis
  • Clustering

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Correspondence to Martha Tatusch .

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Tatusch, M., Klassen, G., Conrad, S. (2020). Loners Stand Out. Identification of Anomalous Subsequences Based on Group Performance. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham.

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  • Print ISBN: 978-3-030-65389-7

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