Haskell-Data-Analysis-Cookbook/Ch08/README.md

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