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Unsupervised Learning with R电子书

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作       者:Erik Rodríguez Pacheco

出  版  社:Packt Publishing

出版时间:2015-12-03

字       数:107.2万

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Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data About This Book Unlock and discover how to tackle clusters of raw data through practical examples in R Explore your data and create your own models from scratch Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide Who This Book Is For This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement. 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 In Detail 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. Style and approach This book takes a step-by-step approach to unsupervised learning concepts and tools, explained in a conversational and easy-to-follow style. Each topic is explained sequentially, explaining the theory and then putting it into practice by using specialized R packages for each topic.
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Unsupervised Learning with R

Table of Contents

Unsupervised Learning with R

Credits

About the Author

Acknowledgments

About the Reviewer

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Welcome to the Age of Information Technology

The information age

Data mining

Machine learning

Supervised learning

Unsupervised learning

Information theory

Entropy

Information gain

Data mining methodology and software tools

CRISP-DM

Benefits of using R

Summary

2. Working with Data – Exploratory Data Analysis

Exploratory data analysis

Loading a dataset

Basic exploration of the dataset

Exploring data by basic visualization

Histograms

Barplots

Boxplots

Special visualizations

Exploring relations in data

Exploration by end-user interfaces

Loading data into Rattle

Basic exploration of dataset in Rattle

Exploring data by graphs in Rattle

Exploring relations in data using Rattle

Summary

3. Identifying and Understanding Groups – Clustering Algorithms

Transforming data

Rescaling data

Recenter

Scale [0-1]

Median/MAD

Natural log

Imputation of missing data

Zero/Missing

Mean imputation

Fundamentals of clustering techniques

The K-Means clustering

Defining the number of clusters

Defining the cluster K-Mean algorithm

Alternatives for plotting clusters

Hierarchical clustering

Clustering distance metric

Linkage methods

Hierarchical clustering in R

Hierarchical clustering with factors

Tips for choosing a hierarchical clustering algorithm

Plotting alternatives for hierarchical clustering

Clustering by end-user interfaces

Summary

4. Association Rules

Fundamentals of association rules

Representation

Exploring the association rules model

Plotting alternatives for association rules

Association rules by end-user tool

Summary

5. Dimensionality Reduction

The curse of dimensionality

Feature extraction

Principal component analysis

Additional visual support for PCA

Advanced tools for plotting PCA

Hierarchical clustering on principal components

Principal components analysis by user interfaces

Summary

6. Feature Selection Methods

Feature selection techniques

Expert knowledge-based techniques

Feature ranking

Subset selection techniques

Embedded methods

Wrapper methods

Filter methods

Summary

A. References

Chapter 1, Welcome to the Age of Information Technology

Chapter 2, Working with Data – Exploratory Data Analysis

Chapter 3, Identifying and Understanding Groups – Clustering Algorithms

Chapter 4, Association Rules

Chapter 5, Dimensionality Reduction

Chapter 6, Feature Selection Methods

Index

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