Orca: A Program for Mining Distance-Based Outliers. Orca is a program for mining outliers in large multivariate data sets. An outlier is an example that is substantially different from the examples in the reminder of the data. An outlier may have values for an attribute that are unusually large or small, or it may have an unusual combination of values that are rarely seen together. Orca mines distance-based outliers. That is, Orca uses the distance from a given example to its nearest neighbors to determine its unusuallness. The intuition is that if there are other examples that are close to the candidate in the feature space, then the example is probably not an outlier. If the nearest examples are substantially different, then the example is likely to be an outlier. Probabilistically, one can view distance-based outliers as identifying candidates that lie at points where the nearest neighbor density estimate is small.

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  1. Ramachandra, Bharathkumar; Dutton, Benjamin; Vatsavai, Ranga Raju: Anomalous cluster detection in spatiotemporal meteorological fields (2019)
  2. Kutsuna, Takuro; Yamamoto, Akihiro: Outlier detection using binary decision diagrams (2017)
  3. Ting, Kai Ming; Washio, Takashi; Wells, Jonathan R.; Aryal, Sunil: Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors (2017)
  4. 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)
  5. Pevný, Tomáš: Loda: lightweight on-line detector of anomalies (2016)
  6. Ahipaşaoğlu, Selin Damla: Fast algorithms for the minimum volume estimator (2015)
  7. Huang, Anqiang; Lai, Kinkeung; Li, Yinhua; Wang, Shouyang: Forecasting container throughput of Qingdao Port with a hybrid model (2015)
  8. Li, Sheng; Shao, Ming; Fu, Yun: Low-rank outlier detection (2014)
  9. Schubert, Erich; Zimek, Arthur; Kriegel, Hans-Peter: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection (2014)
  10. Wells, Jonathan R.; Ting, Kai Ming; Washio, Takashi: LiNearN: a new approach to nearest neighbour density estimator (2014)
  11. Ting, Kai Ming; Zhou, Guang-Tong; Liu, Fei Tony; Tan, Swee Chuan: Mass estimation (2013)
  12. Morik, Katharina; Bhaduri, Kanishka; Kargupta, Hillol: Introduction to data mining for sustainability (2012) ioport
  13. Zimek, Arthur; Schubert, Erich; Kriegel, Hans-peter: A survey on unsupervised outlier detection in high-dimensional numerical data (2012)
  14. Das, Kamalika; Bhaduri, Kanishka; Votava, Petr: Distributed anomaly detection using 1-class SVM for vertically partitioned data (2011)
  15. Angiulli, Fabrizio; Fassetti, Fabio: Distance-based outlier queries in data streams: the novel task and algorithms (2010) ioport
  16. Au, Siu-Tong; Duan, Rong; Hesar, Siamak G.; Jiang, Wei: A framework of irregularity enlightenment for data pre-processing in data mining (2010)
  17. Cheng, Hao; Hua, Kien A.; Yu, Ning: An automatic feature generation approach to multiple instance learning and its applications to image databases (2010) ioport
  18. Koufakou, Anna; Georgiopoulos, Michael: A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes (2010) ioport
  19. Hirose, Shunsuke; Yamanishi, Kenji: Latent variable mining with its applications to anomalous behavior detection (2009)
  20. Li, Wenjia; Parker, James; Joshi, Anupam: Security through collaboration in manets (2009)

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