teda: teda: An implementation of the Typicality and Eccentricity...

In NERC-CEH/teda-r: An Implementation of the Typicality and Eccentricity Data Analysis Framework

Description

The package provides functions to calculate both the batch and recursive typicality and eccentricity values of given observations.

Details

TEDA provides a non-parametric technique to determine how eccentric/typical an observation is with respect to the other observations generated by the same process. Available as either a batch function working over a whole dataset, or as a recursive one-time-pass function that needs the current mean and variance values to be passed as arguments.

Both batch and recursive methods return a datatype (tedab or tedar) which provide print and summary generic function implementations. The batch object also provides a generic plot function.

Further work will implement more of the analytical framework built up around TEDA, such as clustering algorithms.

References

Angelov, P., 2014. Outside the box: an alternative data analytics framework. Journal of Automation Mobile Robotics and Intelligent Systems, 8(2), pp.29-35. DOI: 10.14313/JAMRIS_2-2014/16

Bezerra, C.G., Costa, B.S.J., Guedes, L.A. and Angelov, P.P., 2016, May. A new evolving clustering algorithm for online data streams. In Evolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on (pp. 162-168). IEEE. DOI: 10.1109/EAIS.2016.7502508

NERC-CEH/teda-r documentation built on May 7, 2019, 6:01 p.m.