语言学习笔记K平均算法聚类

1、R语言学习笔记-K平均算法（聚类）k-means ClusteringThis page demonstrates k-means clustering with R.> newiris <- iris> newiris$Species <- NULLApply kmeans to newiris, and store the clustering result in kc. The cluster number is set to 3.> (kc <- kmeans(newiris, 3)K-means clustering with 3 clusters

2、 of sizes 38, 50, 62Cluster means:Sepal.Length Sepal.Width Petal.Length Petal.Width1 6.850000 3.073684 5.742105 2.0710532 5.006000 3.428000 1.462000 0.2460003 5.901613 2.748387 4.393548 1.433871Clustering 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 2 230 2 2 2 2 2 2 2 2 2 2 2 2 2

3、2 2 2 2 2 2 2 2 3 3 1 3 3 3 3 359 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 3 388 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 1 1 1 1 3 1 1 1 1 1 1 3 3 1117 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 1146 1 3 1 1 3Within cluster sum of squares by cluster:1 23.87947 15.15100 39.82097Availa

4、ble components:1 "cluster" "centers" "withinss" "size" Compare the Species label with the clustering result> table(iris$Species, kc$cluster)1 2 3setosa 0 50 0versicolor 2 0 48virginica 36 0 14Plot the clusters and their centres. Note that there are four dim

5、ensions in the data and that only the first two dimensions are used to draw the plot below. Some black points close to the green centre (asterisk) are actually closer to the black centre in the four dimensional space.> plot(newirisc("Sepal.Length", "Sepal.Width"), col=kc$cluster)> points(kc$cent