Based on the example in ?som (did you work through that and look at
the various outputs?), you need to use som.prediction() to get the
predicted class values.
Sarah
On Sat, May 24, 2014 at 1:28 AM, Ashis Deb <ashisde...@gmail.com> wrote:
> Hi all ,
>
>
> I was doing k-mean clustering through which i
> could find out each and every clusters and the contents
> around each cluster centroid like below :-
>
>
> x<-read.csv("normalizedata.csv", header = TRUE) #To read the dataset
>
> km<- kmeans(x, 3, 150)
>
> print(km) <<<---- in this I could get every clusters and
> centroids .
>
> km$cluster <<----- It gave me the clusters which comes
> up during clustering like
>
>
>
>
>
> [1] 3 3 1 2 3 2 1 2 1 3 2 1 3 1 1 1 3 2 1 1 1 1 3 1 1 2 2 2 1 3 3 1 3 3 2
> 2 2
> [38] 3 1 3 1 3 3 1 1 1 3 1 1 2 1 3 3 3 3 3 3 3 3 3 3 1 3 2 3 2 2 3 3 3 3 2
> 3 2
> [75] 1 2 2 1 1 3 3 1 3 1 1 1 1 3 3 2 1 3 3 3 1 3 1 1 1 1 1 2 1 3 1 1 1 2 1
> 3 3
> [112] 1 3 1 2 1 1 1 1 1 1 1 2 2 1 3 1 2 3 1 1 3 2 1 1 1 2 1 3 2 1 3 1 1 1 3
> 2 1
> [149] 3 2 3 3 3 3 1 2 1 1 1 1 1 1 3 2 2 2 1 1 1 3 3 1 1 3 2 1 3 1 1 1 1 1 2
> 1 1
> [186] 3 1 2 1 3 2 3 2 1 2 1 2 3 1 1 1 2 3 2 1 1 2 3 3 1 2 2 1 3 1 1 2 3 3 1
> 1 3
> [223] 3 1 1 3 3 3 2 2 1 3 3 3 2 2 1 2 3 1 1 2 1 1 1 1 1 3 1 3 2 1 1 3 1 1 2
> 2 3
> [260] 1 3 3 3 1 1 2 1 3 1 3 1 2 1 1 1 3 3 2 3 1 1 1 2 1 3 3 1 3 3 1 1 2 1 2
> 3 3
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> But in case of SOM clustering when i had tried like :-
>
>
>
> require(kohonen)
> require (som) #To load Kohonen Package
>
> x<-read.csv("normalizedata.csv",header=TRUE)
>
> x.sc <- scale(x) #To scale
>
> set.seed(7)
>
> xx.som<- SOM(data = x.sc, grid = somgrid(1, 3, "hexagonal")) #som
> function
>
>
>
>
> print(xx.som)
>
>
> I got :-
>
>
>
>
>
>
>
>
> $codes
> hrefCount Number.of.times.Visited Total.time.Spent
> [1,] -0.008926424 0.01642686 -0.01059301
> [2,] -0.008926424 0.01642686 -0.01059301
> [3,] -0.023231402 -0.06730921 -0.02705149
>
>
>
> I hope this are the centroids , I need the cluster results
> like kmeans , how to achieve it , can any one helps .
>
>
>
> THanks
>
> ASHIS
>
--
Sarah Goslee
http://www.functionaldiversity.org
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