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Chapter 8

Chapter 8, Clustering and Classification, involves quintessential analysis methods involving k-means clustering, hierarchical clustering, constructing decision trees, and implementing the k-Nearest Neighbors classifier.

This is the accompanying source code for Haskell Data Analysis Cookbook. Refer to the book for step-by-step explanations.

Recipes:

  • Code01: Implementing the k-means clustering algorithm
  • Code02: Implementing hierarchical clustering
  • Code03: Using a hierarchical clustering library
  • Code04: Finding the number of clusters
  • Code05: Clustering words by their lexemes
  • Code06: Classifying the parts of speech of words
  • Code07: Identifying key words in a corpus of text
  • Code08: Training a parts of speech tagger
  • Code09: Implementing a decision tree classifier
  • Code10: Implementing a k-Nearest Neighbors classifier
  • Code11: Visualizing points using Graphics.EasyPlot

How to use

Setting up the environment

Install the Haskell Platform.

$ sudo apt-get install haskell-platform

Alternatively, install GHC 7.6 (or above) and Cabal.

$ sudo apt-get install ghc cabal-install

Running the code

A Makefile is provided in each recipe. Compile the corresponding executable by running make.

$ make

Run the resulting code. For example,

$ ./Code01

To clean up the directory:

$ make clean