Abstract
Anomaly detection for temporal data has received much attention by many real-world applications. Most existing unsupervised methods dealing with this task are based on a sequential two-way approach (clustering and detection). Because of this, the clustering is less robust to anomalous series in data which distorts the detection step. Thus, to overcome this problem, we propose an embedded technique simultaneously dealing with both methods. We reformulate the task of anomaly detection as a local-weighting-instance clustering problem. The anomalous series are detected locally in each cluster as well as globally in the data, as a whole. Extensive experiments on benchmark datasets are carried out to validate our approach and compare it with other state-of-the-art methods of detection.
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References
Bahadori, M., Kale, D., Yingying, F., Yan, L.: Functional subspace clustering with application to time series. In: Proceedings of ICML, pp. 228–237 (2015)
Budalakoti, S., Srivastava, A., Otey, M.: Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety. IEEE Trans. Syst. Man Cybern. Part C: Appl. 39(1), 101–113 (2009)
Chandola, V., Mithal, V., Kumar, V.: Comparative evaluation of anomaly detection techniques for sequence data. In: Proceedings of ICDM, pp. 743–748 (2008)
Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR time series classification archive (2015). www.cs.ucr.edu/eamonn/time_series_data/
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Ferraty, F., Vieu, P.: Nonparametric Functional Data Analysis: Theory and Practice. Springer, Heidelberg (2006)
Görnitz, N., Braun, L., Kloft, M.: Hidden Markov anomaly detection. In: Proceedings of ICML, pp. 1833–1842 (2015)
Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250–2267 (2014)
Hautamaki, T., Nykanen, P., Frant, P.: Time-series clustering by approximate prototypes. In: Proceedings of ICPR, pp. 1–4 (2008)
Huang, J., Ng, M., Rong, H., Li, Z.: Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27, 657–668 (2005)
Jing, L., Ng, M., Huang, Z.: An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans. Knowl. Data Eng. 19(8), 1026–1041 (2007)
Lane, T., Brodley, C.: Sequence matching and learning in anomaly detection for computer security. In: AAAI Workshop: AI Approaches to Fraud Detection and Risk Management, pp. 43–49 (1997)
Modha, D., Spangler, S.: Feature weighting in k-means clustering. Mach. Learn. 52, 217–237 (2003)
Ng, A., Jordan, M., Weiss, Y.: Analysis and an algorithm. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 849–856. MIT Press (2002)
Petitjean, F., Forestier, G., Webb, G., Nicholson, A., Chen, Y., Keogh, E.: Dynamic time warping averaging of time series allows faster, more accurate classification. In: Proceedings of ICDM, pp. 470–479 (2014)
Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA), pp. 5–8 (2001)
Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)
Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 582–588 (1999)
Vintsyuk, T.: Speech discrimination by dynamic programming. Cybernetics 4(1), 52–57 (1968)
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Benkabou, SE., Benabdeslem, K., Canitia, B. (2017). Local-to-Global Unsupervised Anomaly Detection from Temporal Data. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_59
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DOI: https://doi.org/10.1007/978-3-319-57454-7_59
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