ConOut

Contextual Outlier Detection with Multiple Contexts. The work is accepted for publication and will appear in the proceedings of ECML PKDD 2018.

Website

https://cmuconout.github.io/

Requirements

• Requires Jupyter with R kernel to run the notebooks. Please follow this guide if you are new to R.
• Requires the following python packages:

numpy scipy pandas scikit-learn 

• Requires the following R packages:

coin cluster stats factoextra dplyr corrplot

File Structure

1. The data/ contains the public datasets used in the paper and code/ contains two jupyter notebooks for each dataset.
2. The pre processed features and labels are stored in the serializable RDS format in the data/ folder. The type of the feature (categorical/numerical) is stored in the typevar.RDS
3. For a dataset, the UnifiedMeasure notebook (written in R) computes the unified measure and outputs the contexts and stores it as context.RDS
4. The IsoForest notebook (written in python) incorporates the contexts generated and computes the AUPRC.

Contact

• Meghanath M Y (mmacha@cmu.edu)