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Haskell Data Analysis cookbook

You're reading from Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

Product type Paperback

Published in Jun 2014

Publisher

ISBN-13 9781783286331

Length 334 pages

Edition 1st Edition

Languages

Haskell

Concepts

Data Analysis

Author (1):

Nishant Shukla

View More author details

Table of Contents (14) Chapters

Preface

1. The Hunt for Data FREE CHAPTER

2. Integrity and Inspection

3. The Science of Words

4. Data Hashing

5. The Dance with Trees

6. Graph Fundamentals

7. Statistics and Analysis

8. Clustering and Classification

9. Parallel and Concurrent Design

10. Real-time Data

11. Visualizing Data

12. Exporting and Presenting

Index

Chapter 6. Graph Fundamentals

In this chapter, we will cover the following recipes:

Representing a graph from a list of edges
Representing a graph from an adjacency list
Conducting a topological sort on a graph
Traversing a graph depth-first
Traversing a graph breadth-first
Visualizing a graph using Graphviz
Using Directed Acyclic Word Graphs
Working with hexagonal and square grid networks
Finding maximal cliques in a graph
Determining whether any two graphs are isomorphic

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Authors (1)

Nishant Shukla

Nishant Shukla

Nishant Shukla is a computer scientist with a passion for mathematics. Throughout the years, he has worked for a handful of start-ups and large corporations including WillowTree Apps, Microsoft, Facebook, and Foursquare. Stepping into the world of Haskell was his excuse for better understanding Category Theory at first, but eventually, he found himself immersed in the language. His semester-long introductory Haskell course in the engineering school at the University of Virginia (http://shuklan.com/haskell) has been accessed by individuals from over 154 countries around the world, gathering over 45,000 unique visitors. Besides Haskell, he is a proponent of decentralized Internet and open source software. His academic research in the fields of Machine Learning, Neural Networks, and Computer Vision aim to supply a fundamental contribution to the world of computing.

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