[Skip to content](/content/2017/03/23/clustering-in-r/#content/index.html) help(kmeans) ``` set.seed(1) x=matrix(rnorm(50*2), ncol=2) plot(x) ``` ``` x[1:25,1]=x[1:25,1]+3 x[1:25,2]=x[1:25,2]-4 plot(x) ``` ``` km.out=kmeans(x,2,nstart=20) km.out ``` ``` K-means clustering with 2 clusters of sizes 25, 25 Cluster means: [,1] [,2] 1 0.03223135 0.06924384 2 3.16866521 -3.83459093 Clustering vector: [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Within cluster sum of squares by cluster: [1] 28.53417 50.97988 (between_SS / total_SS = 79.8 %) Available components: [1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" [7] "size" "iter" "ifault" ``` ``` km.out$cluster ``` ``` [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ``` ``` plot(x, col=(km.out$cluster +1), main="K-Means Clustering Results with K=2", xlab="", ylab="", pch=20, cex=2) points(km.out$centers,pch=8,cex=2) ``` ``` set.seed (2) km.out=kmeans(x,3,nstart=20) km.out ``` ``` K-means clustering with 3 clusters of sizes 25, 20, 5 Cluster means: [,1] [,2] 1 0.03223135 0.06924384 2 3.08290361 -4.26589906 3 3.51171162 -2.10935842 Clustering vector: [1] 2 2 2 2 3 3 2 2 2 2 3 2 2 2 2 2 2 3 2 3 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Within cluster sum of squares by cluster: [1] 28.534174 27.901401 3.740301 (between_SS / total_SS = 84.7 %) Available components: [1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" [7] "size" "iter" "ifault" ``` ``` set.seed (3) km.out=kmeans(x,3,nstart=1) km.out$tot.withinss ``` ``` [1] 60.56297 ``` ``` km.out=kmeans(x,3,nstart=20) km.out$tot.withinss ``` ``` [1] 60.37249 ``` ``` iris2 = iris iris2$Species = NULL kmeans.result = kmeans(iris2,3,nstart=20) kmeans.result ``` ``` K-means clustering with 3 clusters of sizes 38, 50, 62 Cluster means: Sepal.Length Sepal.Width Petal.Length Petal.Width 1 6.850000 3.073684 5.742105 2.071053 2 5.006000 3.428000 1.462000 0.246000 3 5.901613 2.748387 4.393548 1.433871 Clustering vector: [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [51] 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 [101] 1 3 1 1 1 1 3 1 1 1 1 1 1 3 3 1 1 1 1 3 1 3 1 3 1 1 3 3 1 1 1 1 1 3 1 1 1 1 3 1 1 1 3 1 1 1 3 1 1 3 Within cluster sum of squares by cluster: [1] 23.87947 15.15100 39.82097 (between_SS / total_SS = 88.4 %) Available components: [1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" [7] "size" "iter" "ifault" ``` ``` table(iris$Species, kmeans.result$cluster) ``` ``` 1 2 3 setosa 0 50 0 versicolor 2 0 48 virginica 36 0 14 ``` ``` plot(iris2[c("Sepal.Length", "Sepal.Width")], col = kmeans.result$cluster) points(kmeans.result$centers[,c("Sepal.Length", "Sepal.Width")], col = 1:3,pch = 8, cex=2) ``` ``` library(cluster) help(pam) pam.result = pam(iris2,3) pam.result ``` ``` Medoids: ID Sepal.Length Sepal.Width Petal.Length Petal.Width [1,] 8 5.0 3.4 1.5 0.2 [2,] 79 6.0 2.9 4.5 1.5 [3,] 113 6.8 3.0 5.5 2.1 Clustering vector: [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [51] 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [101] 3 2 3 3 3 3 2 3 3 3 3 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3 3 2 Objective function: build swap 0.6709391 0.6542077 Available components: [1] "medoids" "id.med" "clustering" "objective" "isolation" "clusinfo" "silinfo" [8] "diss" "call" "data" ``` ``` table(pam.result$clustering,iris$Species) ``` ``` setosa versicolor virginica 1 50 0 0 2 0 48 14 3 0 2 36 ``` ``` plot(pam.result) ``` ``` library(RWeka) help(SimpleKMeans) ``` ### Rate this: i Rate This ### Share this: - [Share on WhatsApp (Opens in new window)WhatsApp](/content/2017/03/23/clustering-in-r/?share=jetpack-whatsapp&nb=1/index.html) - Post Button - [Email a link to a friend (Opens in new window)Email](mailto:?subject=%5BShared%20Post%5D%20Clustering%20in%20R&body=https%3A%2F%2Freckonanalytics.com%2F2017%2F03%2F23%2Fclustering-in-r%2F&share=email&nb=1) LikeLoading... 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