In this example, we use R's cluster analysis functions to determine the clustering in the wheat dataset from http://www.ics.uci.edu/.

The R script we want to use in Jupyter is the following:

# load the wheat data set from uci.edu
wheat <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt", sep="	")
# define useful column names
colnames(wheat) <-c("area", "perimeter", "compactness", "length", "width", "asymmetry", "groove", "undefined")
# exclude incomplete cases from the data
wheat <- wheat[complete.cases(wheat),]
# calculate the clusters
fit <- kmeans(wheat, 5)
fit

Once entered into a notebook, we have something like this:

The resulting generated cluster information is K-means clustering with five clusters of sizes 29, 57, 65, 15, and 32. (Note that, since I had not set the seed value for random number to use, your results may vary.)

Cluster means are:

      area perimeter compactness   length    width asymmetry   
1 16.45345  15.35310   0.8768000 5.882655 3.462517  3.913207 
2 14.06456  14.17175   0.8788158 5.463825 3.211526  2.496354 
3 11.91292  13.26692   0.8496292 5.237477 2.857908  4.844477 
4 19.58333  16.64600   0.8877267 6.315867 3.835067  5.081533 
5 18.95781  16.39563   0.8862125 6.250469 3.742781  2.719813 

Clustering vectors are:

  1   2   3   4   5   6   9  10  11  12  13  14  15  16  17  18  19  20  21  22 
  2   2   2   2   2   2   1   1   1   2   2   2   2   2   2   2   2   2   2   2 
...

Within cluster sum of squares by cluster are:

[1]  54.16095 146.71080 147.29278  25.81297  30.06596
 (between_SS / total_SS =  85.0 %)

The available components are:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"  

So, we generated information about five clusters (the parameter passed in the fit statement).