Binary Gravitational Subspace Search for Outlier Detection in High Dimensional Data Streams
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-22137-8_12
Reference24 articles.
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2. Aggarwal, C.C., Sathe, S.: Theoretical foundations and algorithms for outlier ensembles. ACM SIGKDD Explor. Newsl. 17(1), 24–47 (2015)
3. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)
4. Fouché, E., Böhm, K.: Monte carlo dependency estimation. In: Proceedings of the 31st International Conference on Scientific and Statistical Database Management, pp. 13–24 (2019)
5. Fouché, E., Kalinke, F., Böhm, K.: Efficient subspace search in data streams. Inf. Syst. 97, 101705 (2021)
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