Improving Detection Efficiency: Optimizing Block Size in the Local Outlier Factor (LOF) Algorithm
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50959-9_43
Reference17 articles.
1. Alghushairy, O., Alsini, R., Soule, T., Ma, X.: A review of local outlier factor algorithms for outlier detection in big data streams. Big Data Cogn. Comput. 5, 1 (2021). https://doi.org/10.3390/bdcc5010001
2. Yu, J.X., Qian, W., Lu, H., Zhou, A.: Finding centric local outliers in categorical/numerical spaces. Knowl. Inf. Syst. 9(3), 309–338 (2006). http://dx.doi.org/10.1007/s10115-005-0197-6
3. Taha, A., Hadi, A.S.: Anomaly detection methods for categorical data: a review. ACM Comput. Surv. 52(2), 1–35 (2019). https://doi.org/10.1145/3312739
4. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: SIGMOD 2000: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000). https://doi.org/10.1145/342009.335388
5. Hawkins, D.M.: Identification of Outliers. Chapman and Hall/Springer, London/Dordrecht (1980). https://doi.org/10.1007/978-94-015-3994-4
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