Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions.
This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models.
By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.
publication date
2019-03-27
what you will learn
Implement clustering methods such as k-means, agglomerative, and divisive
Write code in R to analyze market segmentation and consumer behavior
Estimate distribution and probabilities of different outcomes
Implement dimension reduction using principal component analysis
Apply anomaly detection methods to identify fraud
Design algorithms with R and learn how to edit or improve code
key features
Build state-of-the-art algorithms that can solve your business' problems * Learn how to find hidden patterns in your data * Revise key concepts with hands-on exercises using real-world datasets *
approach
Applied Unsupervised Learning with R takes a hands-on approach to using R to reveal the hidden patterns in your unstructured data. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.
audience
Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.
short description
Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.
subtitle
Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
keywords
R, RStudio, machine learning, unsupervised learning, data science, clustering, k-means clustering, principal component analysis, PCA, dimension reduction, analmoly detection, agglomerative, divisive
Product ISBN
9781789956399
