English | 2019 | ISBN: 1789956390 | 320 pages | True Retail (EPUB) | 17.29 MB

Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.

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
Book Description
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 bs 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 sntation 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.

What you will learn
Implement clustering methods such as k-means, agglomerative, and divisive
Write code in R to analyze market sntation 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
Who this book is for
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 bners, it will be beneficial to have some basic, bner-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.

Table of Contents
Introduction to Clustering Methods
Advanced Clustering Methods
Probability Distributions
Dimension Reduction
Data Comparison Methods
Anomaly Detection



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