聚类标签和聚类中心(R中的kmeans) [英] cluster labels and cluster centers (kmeans in R)

问题描述

我是R的新手，正在尝试处理kmeans对象.理想情况下，我想做的是获取数据中每个点的聚类标签列表，并将标签替换为相应的中心.本质上，以一个矩阵结尾，在该矩阵中，每个数据点都由kmeans放置到的群集中心的值来表示.

I am extemely new to R and trying to deal with a kmeans object. Ideally what I would like to do is to take the list of cluster labels for each point in my data and replace the label with the corresponding center. Essentially, ending up with a matrix where each data point is represented by the value of the center of the cluster it has been placed into by kmeans.

有没有一种方法可以有效地做到这一点，而不是手动检查每个条目并将集群标签替换为集群中心值?

Is there a way to do this efficiently instead of going through each entry manually and replacing the cluster label with the cluster center value?

谢谢！

推荐答案

这是您要的吗?扩展自此答案:

Is this what you're after? Extended from this answer:

    # make some data
    x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
               matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
    colnames(x) <- c("x", "y")
    #
    # do cluster analysis
    (cl <- kmeans(x, 2))
    #
    # put cluster labels with data
    out1 <- data.frame(cbind(x, clusterNum = cl$cluster))
    #
    # organise center coords to be ready for merging
    centers <- data.frame(cbind(data.frame(cl$center[,1]), 
                                data.frame(cl$center[,2]), 
                                clusterNum=rownames(cl$center)))
    #
    # merge cluster center coords with data        
    out2 <- merge(out1, centers, all.x = TRUE) 
    #
    # check output
    out2
    clusterNum            x           y   cl.center...1. cl.center...2.
1            1  0.233161364 -0.04258146     0.01064895     0.01376516
2            1 -0.356284774 -0.59135602     0.01064895     0.01376516
3            1 -0.302272796 -0.24033113     0.01064895     0.01376516
4            1 -0.369299302 -0.24997660     0.01064895     0.01376516
5            1 -0.060454427  0.19711328     0.01064895     0.01376516
...
90           2  0.609833599  0.67729922     1.05184887     1.03445718
91           2  0.943306637  1.09420588     1.05184887     1.03445718
92           2  0.545053826  1.22620571     1.05184887     1.03445718
93           2  0.706921965  1.10326091     1.05184887     1.03445718
94           2  0.837644227  1.07121784     1.05184887     1.03445718
95           2  0.550863085  1.06977250     1.05184887     1.03445718
#
# Success! We have one dataframe that includes: raw data, cluster labels
# and cluster center coords

我使用merge将群集中心与原始数据进行协调，但是毫无疑问，还有更有效的方法(例如，不需要重组cl$center).

I used merge to put the cluster center coords with the raw data, but no doubt there are more efficient ways (for example, that don't require cl$center to be reorganised).

这篇关于聚类标签和聚类中心(R中的kmeans)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！