Description Usage Arguments Details Value References See Also Examples

View source: R/teda-recursive.R

A recursive method that takes the state variables of previous mean, previous variance, and the current timestep position, along with the current observation. It returns a teda recursive object. Currently only a univariate implementation.

1 2 | ```
teda_r(curr_observation, previous_mean = curr_observation, previous_var = 0,
k = 1, dist_type = "Euclidean")
``` |

`curr_observation` |
A single observation, the most recent in a series |

`previous_mean` |
The mean value returned by the previous call to this function, if no previous calls, default value is used. |

`previous_var` |
The variance value returned by the previous call to this function, if no previous calls, default value is used. |

`k` |
The count of observations processed by the recursive function, including the current observation |

`dist_type` |
A string representing the distance metric to use, default value (and currently only supported value) is "Euclidean" |

The function has two intended ways of use: on the first pass, it only takes the observation value as a paramter and the rest are provided by defaults, on all other passes, it takes the current observation, the previous mean and variance values, and the current k (number of observations) which includes the current observation.

On return, the teda recursive object holds:

the current observation

the current mean

the current variance

the current observation's eccentricity

the current observation's typicality

the current observation's normalised eccentricity

the current observation's normalised typicality

whether the current observation is an outlier

the current outlier threshold

the next timestep value, k+1

It provides generic functions for print and summary, at this moment both provide the same outout.

The teda recursive object

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

Other TEDA.functions: `teda_b`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |

teda documentation built on May 29, 2017, 8:36 p.m.

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