How to create groups of like sounding names in R?

Question

I'd like to create a group variables based upon how similar a selection of names is. I have started by using the stringdist package to generate a measure of distance. But I'm not sure how to use that output information to generate a group by variable. I've looked at hclust but it seems like to use clustering functions you need to know how many groups you want in the end, and I do not know that. The code I start with is below:

name_list <- c("Mary", "Mery", "Mary", "Joe", "Jo", "Joey", "Bob", "Beb", "Paul")

name_dist <- stringdistmatrix(name_list)
name_dist
name_dist2 <- stringdistmatrix(name_list, method="soundex")
name_dist2

I would like to see a dataframe with two columns that look like

name = c("Mary", "Mery", "Mary", "Joe", "Jo", "Joey", "Bob", "Beb", "Paul")

name_group = c(1, 1, 1, 2, 2, 2, 3, 3, 4)

The groups might be slightly different depending obviously on what distance measure I use (I've suggested two above) but I would probably choose one or the other to run.

Basically, how do I get from the distance matrix to a group variable without knowing the number of clusters I'd like?

Answer 1

You could use a cluster analysis like this:

# loading the package
require(stringdist);

# Group selection by class numbers or height 
num.class <- 5;
num.height <-0.5;

# define names 
n <- c("Mary", "Mery", "Mari", "Joe", 
       "Jo", "Joey", "Bob", "Beb", "Paul");

# calculate distances
d <- stringdistmatrix(n, method="soundex");

# cluster the stuff
h <- hclust(d);

# cut the cluster by num classes
m <- cutree(h, k = num.class);

# cut the cluster by height
p <- cutree(h, h = num.height);

# build the resulting frame
df <- data.frame(names = n, 
                 group.class = m, 
                 group.prob = p);

It produces:

df;
   names group.class group.prob
1  Mary         1          1
2  Mery         1          1
3  Mari         1          1
4   Joe         2          2
5    Jo         2          2
6  Joey         2          2
7   Bob         3          3
8   Beb         4          3
9  Paul         5          4

And the chart gives you an overview:

plot(h, labels=n);

Regards huck