bestMclust: Choose 'best' clusters
In edci: Edge Detection and Clustering in Images

Description Usage Arguments Details Value Author(s) References

Chooses the 'best' regression cluster(s), if the number of true clusters is known.

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  bestMclust(clust, nc = 1, crit = "value")
  projMclust(clust, x, y)
  envMclust(clust, x, y, dist = 0)

`clust`	Cluster object returned by `oregMclust` or `circMclust`.
`nc`	Number of 'best' clusters.
`crit`	Name of the column that should be used to determine the best clusters.
`x,y`	Original observations.
`dist`	Maximal distance of observation from cluster center.

oregMclust and circMclust return a matrix containing not only the parameters of the found clusters but the value of the heights of the corresponding local maxima as well as how often each cluster is found. Both are reasonable criteria for choosing 'best' clusters, which can be done by bestMclust. Additional criteria could be the number of observations projected to each cluster or the number of observations lying in a certain neighbourhood of the cluster center point.

projMclust adds a column proj to clust which contains the number of points belonging to each cluster in the sense that each observation belongs to the cluster with shortest orthogonal distance. If clust is coming from circMclust, a second column projrel is added which contains this number relative to the radius of the particular circle.

envMclust adds a column env to clust which contains the number of observations lying in a dist-neighbourhood of each cluster center. If clust is comming from circMclust a second column envrel is added which contains this number relative to the radius of the particular circle.

A matrix of clusters.

Tim Garlipp, TimGarlipp@gmx.de

Mueller, C. H., & Garlipp, T. (2005). Simple consistent cluster methods based on redescending M-estimators with an application to edge identification in images. Journal of Multivariate Analysis, 92(2), 359–385.

edci documentation built on May 1, 2019, 7:44 p.m.