Aktuelles & Neuigkeiten

11.12.2019 12:27

Publikation

Neuer Konferenzbeitrag zur Cluster-basierten Erkennung von Anomalien

Der Beitrag "Show Me Your Friends and I’ll Tell You Who You Are. Finding Anomalous Time Series by Conspicuous Cluster Transitions" (mit Martha Tatusch, Gerhard Klassen und Stefan Conrad) wurde in "Data Mining", den Proceedings zur 17th Australasian Conference (AusDM 2019) veröffentlicht.

Der Artikel Show Me Your Friends and I’ll Tell You Who You Are. Finding Anomalous Time Series by Conspicuous Cluster Transitions von Martha Tatusch, Gerhard Klassen, Marcus Bravidor und Stefan Conrad wurde in Data Mining, den Proceedings zur 17th Australasian Conference (AusDM 2019), veröffentlicht..

Abstract: The analysis of time series is an important field of research in data mining. This includes different sub areas like trend analysis, outlier detection, forecasting or simply the comparison of multiple time series. Clustering is also an equally important and vast field in time series analysis. Different clustering algorithms provide different analysis aspects like the detection of classes or outliers. There are various approaches how to apply cluster algorithms to time series. Previous work either extracted subsequences or feature sets as an input for cluster algorithms. A rarely used but important approach in clustering of time series is the grouping of data points per point in time. Based on this technique we present a method which analyses the transitions of time series between clusters over time. We evaluate our approach on multiple multivariate time series of different data sets. We discover conspicuous behaviors in relation to groups of sequences and provide a robust outlier detection algorithm.

Tatusch, M., Klassen, G., Bravidor, M., & Conrad, S. (2019). Show Me Your Friends and I’ll Tell You Who You Are. Finding Anomalous Time Series by Conspicuous Cluster Transitions. In: Le, T. et al. (eds.), Data Mining. AusDM 2019. Communications in Computer and Information Science, Vol. 1127. Springer (Singapore), S. 91103. doi: 10.1007/978-981-15-1699-3_8

Der Beitrag ist im Rahmen der Manchot-Forschungsgruppe "Entscheidungsfindung mit Hilfe von Methoden der Künstlichen Intelligenz" entstanden.

Responsible for the content: