kmeansStepAIC: stepwise modelselection of k-means cluster using AIC In kmeansstep: stepwise k-means cluster model selection

Description

stepwise modelselection of k-means cluster using AIC

Usage

 `1` ```kmeansStepAIC(x, centers = 1, iter.max = 10, nstart = 10, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), trace = FALSE) ```

Arguments

 `x` `centers` `iter.max` `nstart` `algorithm` `trace`

Markus Mayer

References

http://sherrytowers.com/2013/10/24/k-means-clustering/

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (x, centers = 1, iter.max = 10, nstart = 10, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), trace = FALSE) { oldAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) centers <- centers + 1 newAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) while (oldAIC > newAIC) { oldAIC <- newAIC centers <- centers + 1 newAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) } return(list(AIC = oldAIC, kmeans = kmeans(x, centers = centers - 1, iter.max, nstart, algorithm, trace))) } ```

kmeansstep documentation built on May 31, 2017, 2:50 a.m.