Draft:Perception Anomaly Detection Algorithm

The Perception algorithm is an unsupervised parameter free machine learning technique designed to detect anomalies in univariate and multivariate data. The algorithm is extremely fast and efficient and provides very good results without the user having to supply any data specific parameters.

The algorithm was inspired by elements of human perception, and builds on the idea that any event that is unexpected to occur, but does occur, is considered an anomaly with respect to some chosen measure.

$\mathbb {E} (C_{n})={S \choose n}{\frac {1}{W^{n-1}}}<1$

Applications

The algorithm has been used in three known production use cases:

1. Detect insider threat activities in corporate networks.
2. Identify start and end points of promotional activity from EPOS data of retailers.
3. Detect unexpected number of support tickets being produced by a node in telecommunication applications.