kmeansStepEllbowAIC: stepwise modelselection of k-means cluster using AIC and... In kmeansstep: stepwise k-means cluster model selection

Description

stepwise modelselection of k-means cluster using AIC and Ellbow-Method

Usage

 `1` ```kmeansStepEllbowAIC(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 23 24 25 26 27 28 29 30 31 32 33 34``` ```##---- 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) { firstAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) centers <- centers + 1 secondAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) centers <- centers + 1 thirdAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) centers <- centers + 1 fourthAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) el_first <- (firstAIC - secondAIC)/(secondAIC - thirdAIC) el_second <- (secondAIC - thirdAIC)/(thirdAIC - fourthAIC) while (el_second > el_first) { firstAIC <- secondAIC secondAIC <- thirdAIC thirdAIC <- fourthAIC centers <- centers + 1 fourthAIC <- kmeansAIC(kmeans(x, centers, iter.max, nstart, algorithm, trace)) el_first <- el_second el_second <- (secondAIC - thirdAIC)/(thirdAIC - fourthAIC) } return(list(AIC = secondAIC, kmeans = kmeans(x, centers = centers - 2, iter.max, nstart, algorithm, trace))) } ```

kmeansstep documentation built on May 2, 2019, 5 p.m.