Is there a way in R to visually show which attribute causes clustering in a hierarchical cluster?
$begingroup$
The hierarchical cluster will group items that are most similar, based on the similarity matrix. But how can you go about determining what about a cluster is similar, and how can you visually show that?
Consider the following code:
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
plot(hc, hang = -1, cex = 0.6)
The table in itself contains information about each state and murder rates, assault rates, population and so on (I'll refer to each column as an attribute). When applying the cluster to the data, we see states like Alabama, Louisiana, Georgia and Tennessee cluster together.
How can i go about determining which attribute causes some states to cluster? For instance, it could be that the four states mentioned above each have a really high assault rate and low murder rate, and similar urban pop. But aside from calculating the deviation of each attribute for each cluster, is there an easier way i can go about determining that, and most importantly visualizing it?
r hierarchical-data-format
asked 12 mins ago
Steven CundenSteven Cunden
62
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Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The hierarchical cluster will group items that are most similar, based on the similarity matrix. But how can you go about determining what about a cluster is similar, and how can you visually show that?
Consider the following code:
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
plot(hc, hang = -1, cex = 0.6)
The table in itself contains information about each state and murder rates, assault rates, population and so on (I'll refer to each column as an attribute). When applying the cluster to the data, we see states like Alabama, Louisiana, Georgia and Tennessee cluster together.
How can i go about determining which attribute causes some states to cluster? For instance, it could be that the four states mentioned above each have a really high assault rate and low murder rate, and similar urban pop. But aside from calculating the deviation of each attribute for each cluster, is there an easier way i can go about determining that, and most importantly visualizing it?
r hierarchical-data-format
asked 12 mins ago
Steven CundenSteven Cunden
62
New contributor
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The hierarchical cluster will group items that are most similar, based on the similarity matrix. But how can you go about determining what about a cluster is similar, and how can you visually show that?
Consider the following code:
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
plot(hc, hang = -1, cex = 0.6)
The table in itself contains information about each state and murder rates, assault rates, population and so on (I'll refer to each column as an attribute). When applying the cluster to the data, we see states like Alabama, Louisiana, Georgia and Tennessee cluster together.
How can i go about determining which attribute causes some states to cluster? For instance, it could be that the four states mentioned above each have a really high assault rate and low murder rate, and similar urban pop. But aside from calculating the deviation of each attribute for each cluster, is there an easier way i can go about determining that, and most importantly visualizing it?
r hierarchical-data-format
asked 12 mins ago
Steven CundenSteven Cunden
62
New contributor
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The hierarchical cluster will group items that are most similar, based on the similarity matrix. But how can you go about determining what about a cluster is similar, and how can you visually show that?
Consider the following code:
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
plot(hc, hang = -1, cex = 0.6)
The table in itself contains information about each state and murder rates, assault rates, population and so on (I'll refer to each column as an attribute). When applying the cluster to the data, we see states like Alabama, Louisiana, Georgia and Tennessee cluster together.
How can i go about determining which attribute causes some states to cluster? For instance, it could be that the four states mentioned above each have a really high assault rate and low murder rate, and similar urban pop. But aside from calculating the deviation of each attribute for each cluster, is there an easier way i can go about determining that, and most importantly visualizing it?
r hierarchical-data-format
r hierarchical-data-format
asked 12 mins ago
Steven CundenSteven Cunden
62
New contributor
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
Steven CundenSteven Cunden
62
New contributor
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
Steven CundenSteven Cunden
62
New contributor
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
Steven CundenSteven Cunden
62
asked 12 mins ago
Steven CundenSteven Cunden
62
62
New contributor
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Steven Cunden is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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