Unsupervised Learning with R

Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data

Unsupervised Learning with R

Learning
Erik Rodríguez Pacheco

7 customer reviews
Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data
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RRP $31.99
RRP $39.99
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Book Details

ISBN 139781785887093
Paperback192 pages

Book Description

The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning.

If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console.

Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques.

By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects.

Table of Contents

Chapter 1: Welcome to the Age of Information Technology
The information age
Information theory
Data mining methodology and software tools
Benefits of using R
Summary
Chapter 2: Working with Data – Exploratory Data Analysis
Exploratory data analysis
Loading a dataset
Basic exploration of the dataset
Exploring data by basic visualization
Exploring relations in data
Exploration by end-user interfaces
Summary
Chapter 3: Identifying and Understanding Groups – Clustering Algorithms
Transforming data
Fundamentals of clustering techniques
Clustering by end-user interfaces
Summary
Chapter 4: Association Rules
Fundamentals of association rules
Exploring the association rules model
Plotting alternatives for association rules
Association rules by end-user tool
Summary
Chapter 5: Dimensionality Reduction
The curse of dimensionality
Feature extraction
Summary
Chapter 6: Feature Selection Methods
Feature selection techniques
Summary

What You Will Learn

  • Load, manipulate, and explore your data in R using techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization
  • Transform your data by using approaches such as scaling, re-centering, scale [0-1], median/MAD, natural log, and imputation data
  • Build and interpret clustering models using K-Means algorithms in R
  • Build and interpret clustering models by Hierarchical Clustering Algorithm’s in R
  • Understand and apply dimensionality reduction techniques
  • Create and use learning association rules models, such as recommendation algorithms
  • Use and learn about the techniques of feature selection
  • Install and use end-user tools as an alternative to programming directly in the R console

Authors

Table of Contents

Chapter 1: Welcome to the Age of Information Technology
The information age
Information theory
Data mining methodology and software tools
Benefits of using R
Summary
Chapter 2: Working with Data – Exploratory Data Analysis
Exploratory data analysis
Loading a dataset
Basic exploration of the dataset
Exploring data by basic visualization
Exploring relations in data
Exploration by end-user interfaces
Summary
Chapter 3: Identifying and Understanding Groups – Clustering Algorithms
Transforming data
Fundamentals of clustering techniques
Clustering by end-user interfaces
Summary
Chapter 4: Association Rules
Fundamentals of association rules
Exploring the association rules model
Plotting alternatives for association rules
Association rules by end-user tool
Summary
Chapter 5: Dimensionality Reduction
The curse of dimensionality
Feature extraction
Summary
Chapter 6: Feature Selection Methods
Feature selection techniques
Summary

Book Details

ISBN 139781785887093
Paperback192 pages
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