Clustering, Classifying, and Working with Weka - Clojure Data Analysis - page 235

See also…
F For information about K-Means clustering, Wikipedia provides a good introduction at
http://en.wikipedia.org/wiki/K-means_clustering
F For information about Weka's SimpleKMeans class, including an explanation of its
options, see http://weka.sourceforge.net/doc.dev/weka/clusterers/
SimpleKMeans.html
F For more about macros in Cl ojure, see http://clojure. org/macros . Also, I've
written a longer tutorial on macros, which is available at http://writingcoding.
blogspot.com/2008/07/stemming-part-8-macros.html
Finding hierarchical clusters in Weka
Another common way to cluster data is the hierarchical way. This involves either splitting the
dataset down to pairs (divisive or top-down) or building the clusters up by pairing the data or
clusters that are closest to each other (agglomerative or bottom-up).
Weka has a class HierarchicalClusterer to perform agglomerative hierarchical
clustering. We'll use the defanalysis macro that we created in the Discovering groups of
data using K-Means clustering recipe to create a wrapper function for this analysis as well.
Getting ready
We'll use the same project.clj dependencies that we did in the Loading CSV and ARFF
iles into Weka recipe, and this set of imports:
(import [weka.core EuclideanDistance]
[weka.clusterers HierarchicalClusterer])
(require '[clojure.string :as str])
Because hierarchical clustering can be memory intensive, we'll use the Iris dataset, which
is fairly small. The easiest way to get this dataset is to download it from http://www.
ericrochester.com/clj-data-analysis/data/UCI/iris.arff . You can also
download it, and other datasets, in a JAR ile from http://weka.wikispaces.com/
Datasets . I loaded it using the load-arff function from the Loading CSV and ARFF iles
into Weka recipe:
(def iris (load-arff "data/UCI/iris.arff"))
We'll also use the defanalysis macro that we deined in the Discovering groups of data
using K-Means clustering recipe.