Code for comparison of PIDForest, KNN, DTM and other algorithms for anomaly detection om basis of AUC-ROC score and AP score

The accompanying paper for DTM explains the algorithm and gives a comparitive study on benchmark algorithms.

The accompanying paper shows that PIDForest performs favorably in comparison to several popular anomaly detection methods, across a broad range of benchmarks. PIDForest also provides a succinct explanation for why a point is labelled anomalous, by providing a set of features and ranges for them which are relatively uncommon in the dataset.

The associated data files in .mat format are also attached. Many of these datasets have additional citation requests if they are useful in your research.