Abstract
In this paper we evaluate competitive learning algorithms in the task of identifying anomalous patterns in time series data. The methodology consists in computing decision thresholds from the distribution of quantization errors produced by normal training data. These thresholds are then used for classifying incoming data samples as normal/abnormal. For this purpose, we carry out performance comparisons among five competitive neural networks (SOM, Kangas’ Model, TKM, RSOM and Fuzzy ART) on simulated and real-world time series data.
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Barreto, G.A., Aguayo, L. (2009). Time Series Clustering for Anomaly Detection Using Competitive Neural Networks. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_4
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DOI: https://doi.org/10.1007/978-3-642-02397-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02396-5
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