Citations & Achievements
========================


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Citing PyOD
-----------


`PyOD paper <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_ is published in
`JMLR <http://www.jmlr.org/>`_ (machine learning open-source software track).
If you use PyOD in a scientific publication, we would appreciate
citations to the following paper::

    @article{zhao2019pyod,
      author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
      title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
      journal = {Journal of Machine Learning Research},
      year    = {2019},
      volume  = {20},
      number  = {96},
      pages   = {1-7},
      url     = {http://jmlr.org/papers/v20/19-011.html}
    }

or::

    Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.


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Scientific Work Using or Referencing PyOD
-----------------------------------------

We are appreciated that PyOD has been increasingly referred and cited in scientific works.
Since its release, PyOD has been used in hundred of academic projects. See `an incomplete list here <https://scholar.google.com/scholar?oi=bibs&hl=en&cites=3726241381117726876>`_.

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Featured Posts & Achievements
-----------------------------

PyOD has been well acknowledged by the machine learning community with a few featured posts and tutorials.

**Analytics Vidhya**: `An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_

**KDnuggets**: `Intuitive Visualization of Outlier Detection Methods <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_

**KDnuggets**: `An Overview of Outlier Detection Methods from PyOD <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>`_

**Towards Data Science**: `Anomaly Detection for Dummies <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_

**Computer Vision News (March 2019)**: `Python Open Source Toolbox for Outlier Detection <https://rsipvision.com/ComputerVisionNews-2019March/18/>`_

**FLOYDHUB**: `Introduction to Anomaly Detection in Python <https://blog.floydhub.com/introduction-to-anomaly-detection-in-python/>`_

**awesome-machine-learning**: `General-Purpose Machine Learning <https://github.com/josephmisiti/awesome-machine-learning#python-general-purpose>`_

**Lecture on anomaly detection with PyOD by Dr.Hadi Fanaee**: `Anomaly Detection Lecture <https://www.youtube.com/watch?v=sF2DeSPrGfc>`_

**Workshop/Showcase using PyOD**:

- `Detecting the Unexpected: An Introduction to Anomaly Detection Methods <http://www.kiss.caltech.edu/workshops/technosignatures/presentations/Wagstaff.pdf>`_, *KISS Technosignatures Workshop* by Dr. Kiri Wagstaff @ Jet Propulsion Laboratory, California Institute of Technology.
  [`Workshop Video <https://www.youtube.com/watch?v=brWqY4Wads4>`_] [`PDF <http://www.kiss.caltech.edu/workshops/technosignatures/presentations/Wagstaff.pdf>`_]


**GitHub Python Trending**:

- 2019: Jul 8th-9th, Apr 5th-6th, Feb 10th-11th, Jan 23th-24th, Jan 10th-14th
- 2018: Jun 15, Dec 8th-9th


**Miscellaneous**:

- `PythonAwesome <https://pythonawesome.com/a-python-toolkit-for-scalable-outlier-detection/>`_
- `awesome-python <https://github.com/uhub/awesome-python>`_
- `PapersWithCode <https://paperswithcode.com/task/anomaly-detection>`_

