无监督分类：将类分配给数据[关闭]

Question

我有一组钻孔数据，每 2 米包含有关不同地质力学特性的信息。我正在尝试创建地质力学域，并将每个点分配给不同的域。

我正在尝试使用随机森林分类，但不确定如何将邻近矩阵（或 randomForest 函数的任何结果）与标签相关联。

到目前为止，我的简陋代码如下：

dh <- read.csv("gt_1_classification.csv", header = T)

#replace all N/A with 0
dh[is.na(dh)] <- 0
library(randomForest)
dh_rf <- randomForest(dh, importance=TRUE, proximity=FALSE, ntree=500, type=unsupervised, forest=NULL)

我希望分类器自行决定域。

任何帮助都会很棒！

Answer 1

Hack-R 是正确的——首先有必要使用一些聚类（无监督学习）方法来探索数据。我提供了一些示例代码，使用 R 内置的 mtcars 数据作为演示：

# Info on the data
?mtcars
head(mtcars)
pairs(mtcars)    # Matrix plot

# Calculate the distance between each row (car with it's variables)
# by default, Euclidean distance = sqrt(sum((x_i - y_i)^2)
?dist
d <- dist(mtcars)
d # Potentially huge matrix

# Use the distance matrix for clustering
# First we'll try hierarchical clustering
?hclust
c <- hclust(d)
c

# Plot dendrogram of clusters
plot(c)

# We might want to try 3 clusters
# need to specify either k = # of groups
groups3 <- cutree(c, k = 3) # "g3" = "groups 3"
# cutree(hcmt, h = 230) will give same result
groups3
# Or we could do several groups at once
groupsmultiple <- cutree(c, k = 2:5)
head(groupsmultiple)

# Draw boxes around clusters
rect.hclust(c, k = 2, border = "gray")
rect.hclust(c, k = 3, border = "blue")
rect.hclust(c, k = 4, border = "green4")
rect.hclust(c, k = 5, border = "darkred")

# Alternatively we can try K-means clustering
# k-means clustering
?kmeans
km <- kmeans(mtcars, 5)
km

# Graph based on k-means
install.packages("cluster")
require(cluster)
clusplot(mtcars, # data frame
     km$cluster, # cluster data
     color = TRUE, # color
     lines = 3, # Lines connecting centroids
     labels = 2) # Labels clusters and cases

在您自己的数据上运行后，请考虑哪种集群定义能够捕捉您感兴趣的相似度。然后，您可以为每个集群创建一个具有“级别”的新变量，然后为此创建一个监督模型。

这是一个使用相同 mtcars 数据的决策树示例。请注意，这里我使用 mpg 作为响应——您可能希望使用基于集群的新变量。

install.packages("rpart")
library(rpart)
?rpart
# grow tree 
tree_mtcars <- rpart(mpg ~ ., method = "anova", data = mtcars)
tree_mtcars <- rpart(mpg ~ ., data = mtcars)

tree_mtcars

summary(tree_mtcars) # detailed summary of splits

# Get R-squared
rsq.rpart(tree_mtcars)
?rsq.rpart

# plot tree 
plot(tree_mtcars, uniform = TRUE, main = "Regression Tree for mpg ")
text(tree_mtcars, use.n = TRUE, all = TRUE, cex = .8)
text(tree_mtcars, use.n = TRUE, all = TRUE, cex = .8)

请注意，虽然信息量很大，但基本决策树通常不适用于预测。如果需要预测，还应探索其他模型。