自动删除计算的聚集层次聚类数据的异常值 [英] Delete outliers automatically of a calculated agglomerative hierarchical clustering data

问题描述

在聚类分析中，可以通过单链接方法轻松识别数据集的异常值.现在，我想自动删除异常值.我的想法是删除超过指定距离值的数据.这是我的代码，其中包含mtcars的示例数据:

in the cluster analysis the outliers of a dataset can be easily identified by the single-linkage method. Now I would like to remove the outliers automatically. My idea is to remove the data which exceed a specified distance value. Here is my code with the example data of mtcars:

library(cluster)
library(dendextend)
cluster<-agnes(mtcars,stand=FALSE,method="single")
dend = as.dendrogram(cluster)

在图解中，您可以看到生成的树状图.最后4辆车("Duster 360"，"Camaro Z28"，"Ford Pantera L"，"Maserati Bora")被识别为异常值，因此我想删除(数据集mtcars的)孔行.如何自动完成?例如.删除高度超过70的行?我尝试了很多方法来消除离群值，但它们似乎不适用于我的数据.

In the Plot you can see the resulting dendrogram. The last 4 cars ("Duster 360", "Camaro Z28", "Ford Pantera L", "Maserati Bora") are identified outliers so I would like to remove their hole rows(of the dataset mtcars). How can I do it automatically? E.g. remove the rows which height is above 70? I've tried a lot of possibilities to remove outliers but they did not seem to be applicable to my data.

非常感谢！

推荐答案

尝试一下:

# your code
library(cluster)
cluster<-agnes(mtcars,stand=FALSE,method="single")
dend = as.dendrogram(cluster)
plot(dend)

#new code    
hclu <- as.hclust(cluster) # convert to list that cutree() understands 
groupindexes <- cutree(hclu, h = 70) # cut at height 70 - creates 3 groups/branches
mtcars[groupindexes != 1,] # "outliers" - not in group 1 but in groups 2 and 3
mtcars[groupindexes == 1,] # all but the 4 "outliers"

结果1-异常值":

                mpg cyl disp  hp drat   wt  qsec vs am gear carb
Duster 360     14.3   8  360 245 3.21 3.57 15.84  0  0    3    4
Camaro Z28     13.3   8  350 245 3.73 3.84 15.41  0  0    3    4
Ford Pantera L 15.8   8  351 264 4.22 3.17 14.50  0  1    5    4
Maserati Bora  15.0   8  301 335 3.54 3.57 14.60  0  1    5    8

结果2:

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
(....and ~30 other rows ....)

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