具有单个输入变量的 kmeans 聚类图
kmeans clustering plot with a single input variable
我有一些数据是这样的；
id_row year_row value
1 1031296 2012 0.13908350
2 1031296 2013 0.11825776
3 1031296 2014 0.03925923
4 1031296 2015 0.07821547
5 1031296 2016 0.04694897
6 1031296 2017 0.07790232
我可以按年份过滤 运行 kmeans
kmdata <- results %>%
filter(year_row == "2010")
km <- kmeans(as.vector(kmdata$value), centers = 4, iter.max = 10, nstart = 1)
km
但是我想计算每年的 kmeans 并查看每个 id_row 随着时间的推移如何改变集群。
由于数据不是矩阵，因此在尝试绘制模型时出现错误。
library(cluster)
clusplot(kmdata$value, km$clusters, color=T, shade=T, labels=2, lines=0)
Error in is.list(s.x.2d) : x is not a data matrix
我使用的方法是"okay"吗？我在网上查看并找到了一些 kmeans 示例，发现许多示例使用多个 inputs 而我只有一个 cosine 相似性输入。
## Murder Assault UrbanPop Rape
## Alabama 1.2426 0.783 -0.521 -0.00342
## Alaska 0.5079 1.107 -1.212 2.48420
## Arizona 0.0716 1.479 0.999 1.04288
数据：
structure(list(id_row = c("1031296", "1031296", "1031296", "1031296",
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您可以使用 nest 创建嵌套的小标题，然后将 kmeans 应用于每个组：
library(tidyverse)
x <- results %>%
as_tibble() %>%
select(-id_row) %>%
group_by(year_row) %>%
nest(.key = "value") %>%
filter(map_int(value, nrow)> 4) %>%
mutate(kmeans = map(value, ~kmeans(.x[[1]], centers = 4, iter.max = 10, nstart = 1)))
请注意，我过滤了一些年份，因为它们没有足够的观察结果。
然后你可以像这样制作一个聚类图：
cluster::clusplot(x$value[[1]], x$kmeans[[1]]$cluster)
我有一些数据是这样的；
id_row year_row value
1 1031296 2012 0.13908350
2 1031296 2013 0.11825776
3 1031296 2014 0.03925923
4 1031296 2015 0.07821547
5 1031296 2016 0.04694897
6 1031296 2017 0.07790232
我可以按年份过滤 运行 kmeans
kmdata <- results %>%
filter(year_row == "2010")
km <- kmeans(as.vector(kmdata$value), centers = 4, iter.max = 10, nstart = 1)
km
但是我想计算每年的 kmeans 并查看每个 id_row 随着时间的推移如何改变集群。
由于数据不是矩阵，因此在尝试绘制模型时出现错误。
library(cluster)
clusplot(kmdata$value, km$clusters, color=T, shade=T, labels=2, lines=0)
Error in is.list(s.x.2d) : x is not a data matrix
我使用的方法是"okay"吗？我在网上查看并找到了一些 kmeans 示例，发现许多示例使用多个 inputs 而我只有一个 cosine 相似性输入。
## Murder Assault UrbanPop Rape
## Alabama 1.2426 0.783 -0.521 -0.00342
## Alaska 0.5079 1.107 -1.212 2.48420
## Arizona 0.0716 1.479 0.999 1.04288
数据：
structure(list(id_row = c("1031296", "1031296", "1031296", "1031296",
"1031296", "1031296", "1031296", "1031296", "1130310", "1130310",
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0.0265279690855791, 0.083416879130688, 0.0463856364022548, 0.131546576568187,
0.058743275128742)), row.names = c(NA, -230L), class = "data.frame")
您可以使用 nest 创建嵌套的小标题，然后将 kmeans 应用于每个组：
library(tidyverse)
x <- results %>%
as_tibble() %>%
select(-id_row) %>%
group_by(year_row) %>%
nest(.key = "value") %>%
filter(map_int(value, nrow)> 4) %>%
mutate(kmeans = map(value, ~kmeans(.x[[1]], centers = 4, iter.max = 10, nstart = 1)))
请注意，我过滤了一些年份，因为它们没有足够的观察结果。
然后你可以像这样制作一个聚类图：
cluster::clusplot(x$value[[1]], x$kmeans[[1]]$cluster)