LOF: Identifying density-based local outliers. For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical.

References in zbMATH (referenced in 108 articles )

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  1. Pei, Yulong; Huang, Tianjin; van Ipenburg, Werner; Pechenizkiy, Mykola: ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks (2022)
  2. Wei, Wenqi; Ouyang, Haibin; Li, Steven; Zhao, Xuanbo; Zou, Dexuan: A modified fireworks algorithm with dynamic search interval based on closed-loop control (2022)
  3. Ali, Bahar; Azam, Nouman; Shah, Anwar; Yao, JingTao: A spatial filtering inspired three-way clustering approach with application to outlier detection (2021)
  4. Kandanaarachchi, Sevvandi; Hyndman, Rob J.: Dimension reduction for outlier detection using DOBIN (2021)
  5. Lebrun-Grandié, D.; Prokopenko, A.; Turcksin, B.; Slattery, S. R.: ArborX. A performance portable geometric search library (2021)
  6. Pang, Guansong; Cao, Longbing; Chen, Ling: Homophily outlier detection in non-IID categorical data (2021)
  7. Talagala, Priyanga Dilini; Hyndman, Rob J.; Smith-Miles, Kate: Anomaly detection in high-dimensional data (2021)
  8. Tan, Wushuang; Liu, Liu: Truncated normal distribution-based EWMA control chart for monitoring the process mean in the presence of outliers (2021)
  9. Vouros, Avgoustinos; Langdell, Stephen; Croucher, Mike; Vasilaki, Eleni: An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations (2021)
  10. Xie, Yunxin; Zhu, Chenyang; Hu, Runshan; Zhu, Zhengwei: A coarse-to-fine approach for intelligent logging lithology identification with extremely randomized trees (2021)
  11. Yan, Yan; Herman, Eyeleko Anselme; Mahmood, Adnan; Feng, Tao; Xie, Pengshou: A weighted K-member clustering algorithm for K-anonymization (2021)
  12. Zhang, Lin; Zhang, Wenyu; McNeil, Maxwell J.; Chengwang, Nachuan; Matteson, David S.; Bogdanov, Petko: AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series (2021)
  13. Zhang, Ying; Zhou, Baohang; Ding, Xiaoke; Ouyang, Jiawei; Cai, Xiangrui; Gao, Jinyang; Yuan, Xiaojie: Adversarially learned one-class novelty detection with confidence estimation (2021)
  14. D’Urso, Pierpaolo; De Giovanni, Livia; Massari, Riccardo: Smoothed self-organizing map for robust clustering (2020)
  15. Filzmoser, Peter; Gregorich, Mariella: Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers (2020)
  16. Iwata, Tomoharu; Toyoda, Machiko; Tora, Shotaro; Ueda, Naonori: Anomaly detection with inexact labels (2020)
  17. Kandanaarachchi, Sevvandi; Muñoz, Mario A.; Hyndman, Rob J.; Smith-Miles, Kate: On normalization and algorithm selection for unsupervised outlier detection (2020)
  18. Naitzat, Gregory; Zhitnikov, Andrey; Lim, Lek-Heng: Topology of deep neural networks (2020)
  19. Riahi, Fatemeh; Schulte, Oliver: Model-based exception mining for object-relational data (2020)
  20. Shubhranshu Shekhar, Neil Shah, Leman Akoglu: FairOD: Fairness-aware Outlier Detection (2020) arXiv

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