Otwarty dostęp

Anomaly Pattern Detection in Streaming Data Based on the Transformation to Multiple Binary-Valued Data Streams


Zacytuj

[1] D. Hawkins. Identification of outliers. Springer Netherlands, 1980.10.1007/978-94-015-3994-4 Search in Google Scholar

[2] C.H. Park. Outlier and anomaly pattern detection on data streams. The Journal of Supercomputing, 75:6118–6128, 2019.10.1007/s11227-018-2674-1 Search in Google Scholar

[3] T. Kim and C.H. Park. Anomaly pattern detection for streaming data. Expert Systems with Applications, 149, 2020.10.1016/j.eswa.2020.113252 Search in Google Scholar

[4] F. Liu, K. Ting, and Z. Zhou. Isolation forest. In Proceedings of the 8th International Conference on Data Mining, 2008.10.1109/ICDM.2008.17 Search in Google Scholar

[5] Q. Feng, Y. Zhang, C. Li, Z. Dou, and J. Wang. Anomaly detection of spectrum in wireless communication via deep auto-encoders. The Journal of Supercomputing, 73(7):3161–3178, 2017.10.1007/s11227-017-2017-7 Search in Google Scholar

[6] P. Remy. Anomaly detection in time setries using auto encoders. bolg positng from http://philipperemy.github.io/anomaly-detection. Search in Google Scholar

[7] D. Pokrajac, A. Lazarevic, and L.J. Latecki. Incremental local outlier detection for data streams. In Proceedings of the CIDM, 2007.10.1109/CIDM.2007.368917 Search in Google Scholar

[8] C. Aggarwal. Outlier analysis. Springer, 2017.10.1007/978-3-319-47578-3 Search in Google Scholar

[9] D. Padilla, R. Brinkworth, and M. McDonnell. Performance of a hierarchical temporal memory network in noisy sequence learning. In Proceedings of IEEE international conference on computational intelligence and cybernetics, 2013.10.1109/CyberneticsCom.2013.6865779 Search in Google Scholar

[10] S. Ahmad and S. Purdy. Real-time anomaly detection for streaming analytics, 2016. Retrieved from https://arxiv.org/pdf/1607.02480.pdf. Search in Google Scholar

[11] W. Wong, A. Moore, G. Cooper, and M. Wagner. Rule-based anomaly pattern detection for detecting disease outbreaks. In Proceedings of the 18th International Conference on Artificial Intelligence, 2002. Search in Google Scholar

[12] K. Das, J. Schneider, and D. Neil. Anomaly pattern detection in categorical datasets. In Proceedings of KDD, 2008.10.1145/1401890.1401915 Search in Google Scholar

[13] F. et al. Pedregosa. Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12:2825–2830, 2011. Search in Google Scholar

[14] M.M. Breunig, H-P. Kriegel, R.T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In Proceedings of the 2000 ACM Sigmod International Conference on Management of Data, 2000.10.1145/342009.335388 Search in Google Scholar

[15] P. Tan, M. Steinbach, and V. Kumar. Introduction to data mining. Addison Wesley, Boston, 2006. Search in Google Scholar

[16] S. Hawkins, H. Hongxing, G. Williams, and R. Baxter. Outlier detection using replicator neural networks. In Proceedings of the International Conference on Data Warehousing and Knowledge Discovery, 2002.10.1007/3-540-46145-0_17 Search in Google Scholar

[17] A. Bife, G. Holmes, R. Kirkby, and B. Pfahringer. Moa: Massive online analysis. Journal of Machine Learning Research, 11:1601–1604, 2010. Search in Google Scholar

[18] Y. Zhao, Z. Nasrullah, and Z. Li. Pyod: A python toolbox for scalable outlier detection. Journal of Machine Learning Research, 20(96):1–7, 2019. Search in Google Scholar

eISSN:
2449-6499
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Computer Sciences, Databases and Data Mining, Artificial Intelligence