A Notebook where I implement differents Unsupervised anomaly detection algorithms on a simple exemple. The goal was to understand how the different algorithms works and their differents caracteristics. I have created this notebook after reading this article: https://iwringer.wordpress.com/2015/11/17/anomaly-detection-concepts-and-techniques/ I implement the algorithms cited in the article and add some.
- Cluster based anomaly detection (K-mean)
- Repartition of data into categories then Gaussian/Elliptic Enveloppe on each categories separately
- Markov Chain
- Isolation Forest
- One class SVM
- RNN (comparison between prediction and reality)