From b2648512772a89999c352b507753e02770e80163 Mon Sep 17 00:00:00 2001 From: Yue Zhao Date: Tue, 27 Nov 2018 22:02:00 -0500 Subject: [PATCH] Update reference --- README.rst | 61 +++++++++++++++++++++++++----------------------------- 1 file changed, 28 insertions(+), 33 deletions(-) diff --git a/README.rst b/README.rst index 070579032..5b1f5ce5c 100644 --- a/README.rst +++ b/README.rst @@ -63,7 +63,7 @@ PyOD is a comprehensive and scalable **Python toolkit** for **detecting outlying multivariate data. This exciting yet challenging field is commonly referred as `Outlier Detection `_ or `Anomaly Detection `_. -Since 2017, PyOD has been successfully used in various academic researches [4, 8, 17] and commercial products. +Since 2017, PyOD has been successfully used in various academic researches [#Zhao2018DCSO]_ [#Zhao2018XGBOD]_ [#Zhao2019LSCP]_ and commercial products. PyOD is featured for: @@ -98,7 +98,7 @@ Key Links and Resources ^^^^^^^^^^^^^^^^^^^^^^^ -* `View the latest codes on Github `_ +* `View the latest codes on Github `_ * `Execute Interactive Jupyter Notebooks `_ * `Anomaly Detection Resources `_ @@ -137,30 +137,30 @@ detection utility functions. #. Probabilistic Models for Outlier Detection: - #. **ABOD: Angle-Based Outlier Detection** [7] - #. **FastABOD: Fast Angle-Based Outlier Detection using approximation** [7] + #. **ABOD: Angle-Based Outlier Detection** [#Kriegel2008Angle]_ + #. **FastABOD: Fast Angle-Based Outlier Detection using approximation** [#Kriegel2008Angle]_ #. Outlier Ensembles and Combination Frameworks - #. **Isolation Forest** [2] - #. **Feature Bagging** [9] + #. **Isolation Forest** [#Liu2008Isolation]_ + #. **Feature Bagging** [#Lazarevic2005Feature]_ #. Neural Networks and Deep Learning Models (implemented in Keras) - #. **AutoEncoder with Fully Connected NN** [16, Chapter 3] + #. **AutoEncoder with Fully Connected NN** [#Aggarwal2015Outlier]_ [Chapter 3] FAQ regarding AutoEncoder in PyOD and debugging advice: `known issues `_ **Outlier Detector/Scores Combination Frameworks**: -#. **Feature Bagging**\ : build various detectors on random selected features [9] -#. **Average** & **Weighted Average**\ : simply combine scores by averaging [6] +#. **Feature Bagging**\ : build various detectors on random selected features [#Lazarevic2005Feature]_ +#. **Average** & **Weighted Average**\ : simply combine scores by averaging [#Aggarwal2015Theoretical]_ #. **Maximization**\ : simply combine scores by taking the maximum across all - base detectors [6] -#. **Average of Maximum (AOM)** [6] -#. **Maximum of Average (MOA)** [6] -#. **Threshold Sum (Thresh)** [6] + base detectors [#Aggarwal2015Theoretical]_ +#. **Average of Maximum (AOM)** [#Aggarwal2015Theoretical]_ +#. **Maximum of Average (MOA)** [#Aggarwal2015Theoretical]_ +#. **Threshold Sum (Thresh)** [#Aggarwal2015Theoretical]_ **Comparison of all implemented models** are made available below: (\ `Figure `_\ , @@ -460,34 +460,23 @@ Reference ^^^^^^^^^ +.. [#Aggarwal2015Outlier] Aggarwal, C.C., 2015. Outlier analysis. In Data mining (pp. 237-263). Springer, Cham. -[4] Y. Zhao and M.K. Hryniewicki, "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles," *ACM SIGKDD Workshop on Outlier Detection De-constructed (ODD v5.0)*\ , 2018. +.. [#Aggarwal2015Theoretical] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.\ *ACM SIGKDD Explorations Newsletter*\ , 17(1), pp.24-47. -.. [#Goldstein2012Histogram] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In *KI-2012: Poster and Demo Track*\ , pp.59-63. - -[6] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.\ *ACM SIGKDD Explorations Newsletter*\ , 17(1), pp.24-47. - -[7] Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In *KDD '08*\ , pp. 444-452. ACM. - -[8] Y. Zhao and M.K. Hryniewicki, "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning," *IEEE International Joint Conference on Neural Networks*\ , 2018. +.. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery* pp. 15-27. -[9] Lazarevic, A. and Kumar, V., 2005, August. Feature bagging for outlier detection. In *KDD '05*. 2005. +.. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. *ACM Sigmod Record*\ , 29(2), pp. 93-104. -.. [#Shyu2003A] Shyu, M.L., Chen, S.C., Sarinnapakorn, K. and Chang, L., 2003. A novel anomaly detection scheme based on principal component classifier. *MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING*. +.. [#Goldstein2012Histogram] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In *KI-2012: Poster and Demo Track*\ , pp.59-63. +.. [#Hardin2004Outlier] Hardin, J. and Rocke, D.M., 2004. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. *Computational Statistics & Data Analysis*\ , 44(4), pp.625-638. .. [#He2003Discovering] He, Z., Xu, X. and Deng, S., 2003. Discovering cluster-based local outliers. *Pattern Recognition Letters*\ , 24(9-10), pp.1641-1650. -[16] Aggarwal, C.C., 2015. Outlier analysis. In Data mining (pp. 237-263). Springer, Cham. +.. [#Kriegel2008Angle] Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In *KDD '08*\ , pp. 444-452. ACM. -[17] Zhao, Y., Hryniewicki, M.K., Nasrullah, Z., and Li, Z. SCP: Selective Combination in Parallel Outlier Ensembles. *SIAM International Conference on Data Mining (SDM)*. **Currently Under Review**. - - ----- - -.. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery* pp. 15-27. - -.. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. *ACM Sigmod Record*\ , 29(2), pp. 93-104. +.. [#Lazarevic2005Feature] Lazarevic, A. and Kumar, V., 2005, August. Feature bagging for outlier detection. In *KDD '05*. 2005. .. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\ , pp. 413-422. IEEE. @@ -497,4 +486,10 @@ Reference .. [#Rousseeuw1999A] Rousseeuw, P.J. and Driessen, K.V., 1999. A fast algorithm for the minimum covariance determinant estimator. *Technometrics*\ , 41(3), pp.212-223. -.. [#Hardin2004Outlier] Hardin, J. and Rocke, D.M., 2004. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. *Computational Statistics & Data Analysis*\ , 44(4), pp.625-638. +.. [#Shyu2003A] Shyu, M.L., Chen, S.C., Sarinnapakorn, K. and Chang, L., 2003. A novel anomaly detection scheme based on principal component classifier. *MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING*. + +.. [#Zhao2018DCSO] Zhao, Y. and Hryniewicki, M.K. DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles. *ACM SIGKDD Workshop on Outlier Detection De-constructed (ODD v5.0)*\ , 2018. + +.. [#Zhao2018XGBOD] Zhao, Y. and Hryniewicki, M.K. XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning. *IEEE International Joint Conference on Neural Networks*\ , 2018. + +.. [#Zhao2019LSCP] Zhao, Y., Hryniewicki, M.K., Nasrullah, Z., and Li, Z. LSCP: Locally Selective Combination of Parallel Outlier Ensembles. *SIAM International Conference on Data Mining (SDM)*. **Currently Under Review**.