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
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.