Description Usage Arguments Value Note References See Also Examples

View source: R/ktaucenters_aux.R

Robust Clustering algorithm based on centers, a robust and efficient version of KMeans.

1 | ```
ktaucenters_aux(X, K, centers, tolmin, NiterMax)
``` |

`X` |
A matrix of size n x p. |

`K` |
The number of clusters. |

`centers` |
matrix of size K x p containing the K initial centers, one at each matrix-row. |

`tolmin` |
tolerance parameter used for the algorithm stopping rule |

`NiterMax` |
a maximun number of iterations used for the algorithm stopping rule |

A list including the estimated K centers and labels for the observations

`centers`

: matrix of size K x p, with the estimated K centers.`cluster`

: array of size n x 1 integers labels between 1 and K.`tauPath`

: sequence of tau scale values at each iterations.`Wni`

: numeric array of size n x 1 indicating the weights associated to each observation.`emptyClusterFlag`

: a boolean value. True means that in some iteration there were clusters totally empty`niter`

: number of iterations untill convergence is achived or maximun number of iteration is reached`di`

distance of each observation to its assigned cluster-center

Some times, if the initial centers are wrong, the algorithm converges to a non-optimal (local) solution.
To avoid that, the algorithm must be run several times. This task is carried out by `ktaucenters`

Gonzalez, J. D., Yohai, V. J., & Zamar, R. H. (2019). Robust Clustering Using Tau-Scales. arXiv preprint arXiv:1906.08198.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# Generate Sintetic data (three cluster well separated)
Z=rnorm(600);
mues=rep(c(0,10,20),200)
X= matrix(Z+mues,ncol=2)
# Applying the algortihm
sal = ktaucenters_aux(
X, K=3, centers=X[sample(1:300,3), ],
tolmin=1e-3, NiterMax=100)
#plot the results
plot(X,type="n")
points(X[sal$cluster==1,],col=1);
points(X[sal$cluster==2,],col=2);
points(X[sal$cluster==3,],col=3);
``` |

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