Book Image

Clojure Data Analysis Cookbook

By : Eric Rochester
Book Image

Clojure Data Analysis Cookbook

By: Eric Rochester

Overview of this book

<p>Data is everywhere and it's increasingly important to be able to gain insights that we can act on. Using Clojure for data analysis and collection, this book will show you how to gain fresh insights and perspectives from your data with an essential collection of practical, structured recipes.<br /><br />"The Clojure Data Analysis Cookbook" presents recipes for every stage of the data analysis process. Whether scraping data off a web page, performing data mining, or creating graphs for the web, this book has something for the task at hand.<br /><br />You'll learn how to acquire data, clean it up, and transform it into useful graphs which can then be analyzed and published to the Internet. Coverage includes advanced topics like processing data concurrently, applying powerful statistical techniques like Bayesian modelling, and even data mining algorithms such as K-means clustering, neural networks, and association rules.</p>
Table of Contents (18 chapters)
Clojure Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Finding hierarchical clusters in Weka


Another common way to cluster data is hierarchically. This involves either splitting the dataset down to pairs or building the clusters up by pairing the data or clusters that are closest to each other.

Weka has a class—HierarchicalClustererfor performing 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 also.

Getting ready

We'll use the same project.clj dependencies that we did in the Loading CSV and ARFF data into Weka recipe. And we'll use the following 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...