fviz_cluster()不接受k-medoid(PAM)结果 [英] fviz_cluster() not accepting for k-medoid (PAM) results

问题描述

尝试使用 fviz_cluster()可视化k-medoid(PAM)簇结果，但是函数不接受它们.

Trying to visualize k-medoid (PAM) cluster results with fviz_cluster(), however function isn't accepting them.

它在?fviz_clust 内声明"object arguments =类" partition"的对象；由群集包中的 pam()， clara()或 fanny()函数创建"

It states within ?fviz_clust "object argument = an object of class "partition" created by the functions pam(), clara() or fanny() in cluster package"

我尝试通过其他方式访问聚类向量；

I've tried accessing the clustering vector through other means;

pam_gower_2$clustering
pam_gower_2[[3]]

但是然后我得到一个单独的错误:

but then I get a separate error:

错误:$运算符对于原子向量无效"

Error: $ operator is invalid for atomic vectors"

pam_gower_2的类是分区吗?正如争论所期望的那样.

The class of pam_gower_2 is partition? As the argument expects.

class(pam_gower_2)
> class(pam_gower_2)
[1] "pam"       "partition"

这是我正在使用的代码:

Here's the code I'm using:

df_gower <- df[, c(2:21)] 
df_gower <- df_gower[, c(1:4, 11:12, 14:15, 5:10, 16:20)] 

gower_dist <- daisy(df_gower, metric="gower", type=list(ordratio=c(2:4, 6), symm=c(7:8), asymm=c(5), logratio=c(13)))

gower_mat <- as.matrix(gower_dist)
tendency_gower <- get_clust_tendency(gower_mat, 100, graph=T)
tendency_gower$hopkins_stat

fviz_nbclust(gower_mat, pam, method="wss")
fviz_nbclust(gower_mat, pam, method="silhouette")

pam_gower_2 <- pam(gower_mat, k=2, diss=T)

# all of the above functions as expected

fviz_cluster(pam_gower_2, gower_mat)

以上行会产生以下错误:

above line produces the following error:

如果(！is.null(names(x)))list(names(x)，:数据"必须是矢量类型，为"NULL"

Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), :'data' must be of a vector type, was 'NULL'

将非常感谢反馈/修复，为何不起作用的原因或可视化的替代方法.

Would greatly appreciate feedback/ fix, reasons as to why this doesn't work, or an alternative method for visualizing.

谢谢:)

推荐答案

以下是 fviz_cluster 的文档:

data:已用于集群的数据.仅当object是kmeans或dbscan类时才需要.

data: the data that has been used for clustering. Required only when object is a class of kmeans or dbscan.

因此，您只需要将 pam 的结果传递给 fviz_cluster .

You therefore only need to pass the results of pam to fviz_cluster.

这是 fviz_cluster 和 pam 的最小示例:

library("factoextra")
library("cluster")

data("USArrests")
res <- pam(USArrests, 4)
fviz_cluster(res)

如果将 pam 应用于距离矩阵，则会出现错误.一种解决方法是事后设置结果的 data 字段.这是使用距离矩阵( diss )的修改示例:

If you apply pam with a distance matrix, you have your error. A workaround is to set the data field of the result afterwards. Here is the modified example using a distance matrix (diss):

library("factoextra")
library("cluster")

data("USArrests")

diss = dist(USArrests)
res <- pam(diss, 4)

res$data = USArrests
fviz_cluster(res)

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