Learning Data Mining with R


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Learning Data Mining with R
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Overview
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Sample Chapters
  • Develop a sound strategy for solving predictive modeling problems using the most popular data mining algorithms
  • Gain understanding of the major methods of predictive modeling
  • Packed with practical advice and tips to help you get to grips with data mining

Book Details

Language : English
Paperback : 380 pages [ 235mm x 191mm ]
Release Date : December 2014
ISBN : 1783982101
ISBN 13 : 9781783982103
Author(s) : Bater Makhabel
Topics and Technologies : All Books, Big Data and Business Intelligence, Open Source
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What you will learn from this book

  • Discover how you can manipulate data with R using code snippets
  • Learn the top classification algorithms written in R
  • Develop best practices in the fields of graph mining and network analysis
  • Find out the solutions for mining text and web data with appropriate support from R
  • Familiarize yourself with the algorithm written in R for Spatial Data Mining, Text Mining and Web Data Mining
  • Explore solutions written in R based on R Hadoop projects

In Detail

The necessity to handle many, complex statistical analysis projects is hitting statisticians and analysts across the globe. With increasing interest in data analysis, R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers functionality for dealing with a truly vast array of problems.

Being able to deal with the array of problems that you may encounter during complex statistical projects can be difficult. This book will provide those with only a basic knowledge of R the skills and knowledge to successfully create and customize the most popular data mining algorithms to overcome these difficulties.

Learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Finish this book feeling confident in the ability to know which data mining algorithm to apply in any situation.

Approach

This is an easy-to-follow guide that will walk you through the development process and comprehensive real-world data analysis projects using R.

Who this book is for

This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.

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