r - 基于多个变量的聚类观察

Question

我正在寻找一个 r 函数来基于两个变量在我的数据集中创建集群（希望“集群”是我想做的正确名称）。变量_1 或变量_2 具有相同值的每两个观察值应位于同一个集群中。在下面的简短示例中，我根据 variable_1 和 variable_2 聚合数据帧 df。

df <- data.frame(variable_1=c("a","a","b","b","c","c","d","d","e","e"),variable_2=c("g1","g2","g1","g3","g2","g4","g4","g6","g7","g8"),value=rnorm(10))

df$clusters <- some_function_to_create_clusters(df[,c("variable_1","variable_2")])

结果应如下所示：

df$clusters <- c("clu1","clu1","clu1","clu1","clu1","clu1","clu1","clu1","clu2","clu2")
df

请注意，第一个集群包含 variable_1 等于“a”、“b”、“c”或“d”的每个人：“a”和“b”合并在一起，因为它们共享“g1”（第 1 行和第 3 行）；“a”和“c”被合并，因为它们共享“g2”（第 2 行和第 5 行）；和 "c" 和 "d" 被合并，因为它们共享 "g4"（第 6 行和第 7 行）。最后，在最后一个集群中，只有 variable_1=="e" 的观察值，因为它们不与任何人共享 variable_2。

只是为了澄清我打算做什么，我会更好地解释我的问题集。我正在将县与附近的旅游景点配对。不同的县周围有不同的旅游景点（TA），同一个县周围有很多旅游景点。但是，这个由县和 TA 组成的“旅游集群”在该国很少分布。请注意，由于县与旅游景点连接的“连锁”效应，一些遥远的县可能在同一个集群内。所以我想根据县和旅游景点的 id 找到那些“集群”。

这看起来很简单，但我无法弄清楚如何实现。

非常感谢

标签： raggregatecluster-computing

Answer 1

igraph solution

Disclaimer: I am completely new to igraph, so there's probably a better solution to this problem. However this seems to work.

With the igraph package we can cluster the data using the graph_from_data_frame() function, and then extract the clusters with components. You get the added advantage of being able to visualise the clusters.

library(igraph)

graph <- graph_from_data_frame(df[, 1:2], directed = FALSE)

cmp <- components(graph)$membership

df$cluster <- cmp[df$variable_1]

plot(graph)

Wrapping it up into a function

If you wanted to wrap it up as a function, something like this works:

find_clusters <- function(x, y) {
  edges <- data.frame(from = x, to = y)
  graph <- igraph::graph_from_data_frame(edges, directed = FALSE)
  cmp <- igraph::components(graph)$membership
  return(cmp[x])
}

Using the additional example you posted as a comment above, we thus have the following workflow:

library(dplyr)

df <- data.frame(
    variable_1 = c("a", "a", "b", "b", "c", "c", "d", "d", "e", "e", "f", "f"),
    variable_2 = c( "g1", "g2", "g1", "g3", "g2", "g4", "g4", "g6", "g7", "g8", "g9", "g12"),
    value = rnorm(12)
  )

df %>% 
  mutate(cluster = find_clusters(variable_1, variable_2))

#    variable_1 variable_2       value  cluster
# 1           a         g1 -0.03410073        1
# 2           a         g2  0.51261548        1
# 3           b         g1  0.06470451        1
# 4           b         g3 -1.97228101        1
# 5           c         g2 -0.39751063        1
# 6           c         g4  0.17761619        1
# 7           d         g4 -0.13771207        1
# 8           d         g6 -0.72183017        1
# 9           e         g7  0.09012701        2
# 10          e         g8  0.45763593        2
# 11          f         g9 -0.83172613        3
# 12          f        g12  2.83480352        3