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