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.

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  1. Filzmoser, Peter; Gregorich, Mariella: Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers (2020)
  2. Iwata, Tomoharu; Toyoda, Machiko; Tora, Shotaro; Ueda, Naonori: Anomaly detection with inexact labels (2020)
  3. Riahi, Fatemeh; Schulte, Oliver: Model-based exception mining for object-relational data (2020)
  4. Brodinová, Šárka; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian; Rohm, Maia: Robust and sparse k-means clustering for high-dimensional data (2019)
  5. Eiras-Franco, Carlos; Martínez-Rego, David; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio: Large scale anomaly detection in mixed numerical and categorical input spaces (2019)
  6. Łukasik, Szymon; Lalik, Konrad; Sarna, Piotr; Kowalski, Piotr A.; Charytanowicz, Małgorzata; Kulczycki, Piotr: Efficient astronomical data condensation using approximate nearest neighbors (2019)
  7. Yue Zhao, Zain Nasrullah, Zheng Li: PyOD: A Python Toolbox for Scalable Outlier Detection (2019) arXiv
  8. Brodinová, Šárka; Zaharieva, Maia; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian: Clustering of imbalanced high-dimensional media data (2018)
  9. Lin, Chaoguang; Zhu, Qiuhan; Guo, Shunan; Jin, Zhuochen; Lin, Yu-Ru; Cao, Nan: Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis (2018)
  10. Luca, Stijn E.; Pimentel, Marco A. F.; Watkinson, Peter J.; Clifton, David A.: Point process models for novelty detection on spatial point patterns and their extremes (2018)
  11. Poddar, Sanjoli; Patra, Bidyut Kr.: Reduction in execution cost of (k)-nearest neighbor based outlier detection method (2018)
  12. Szczepaniak, P. S.; Duraj, A.: Case-based reasoning: the search for similar solutions and identification of outliers (2018)
  13. Angiulli, Fabrizio; Fassetti, Fabio; Manco, Giuseppe; Palopoli, Luigi: Outlying property detection with numerical attributes (2017)
  14. Bardet, Jean-Marc; Dimby, Solohaja-Faniaha: A new non-parametric detector of univariate outliers for distributions with unbounded support (2017)
  15. Goix, Nicolas; Sabourin, Anne; Clémençon, Stephan: Sparse representation of multivariate extremes with applications to anomaly detection (2017)
  16. Kutsuna, Takuro; Yamamoto, Akihiro: Outlier detection using binary decision diagrams (2017)
  17. Şeref, Onur; Razzaghi, Talayeh; Xanthopoulos, Petros: Weighted relaxed support vector machines (2017)
  18. Ting, Kai Ming; Washio, Takashi; Wells, Jonathan R.; Aryal, Sunil: Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors (2017)
  19. Wang, Xite; Bai, Mei; Shen, Derong; Nie, Tiezheng; Kou, Yue; Yu, Ge: A distributed algorithm for the cluster-based outlier detection using unsupervised extreme learning machines (2017)
  20. Deng, Tingquan; Yang, Jinhong: An improved semisupervised outlier detection algorithm based on adaptive feature weighted clustering (2016)

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