Alok Malik - Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
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- Book:Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
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Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA: summary, description and annotation
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Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.
Key FeaturesStarting 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, youll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. Youll 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, youll 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 learnApplied 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.
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Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
Alok Malik and Bradford Tuckfield
Copyright 2019 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Authors: Alok Malik and Bradford Tuckfield
Technical Reviewer: Smitha Shivakumar
Managing Editor: Rutuja Yerunkar
Acquisitions Editor: Aditya Date
Production Editor: Nitesh Thakur
Editorial Board: David Barnes, Ewan Buckingham, Shivangi Chatterji, Simon Cox, Manasa Kumar, Alex Mazonowicz, Douglas Paterson, Dominic Pereira, Shiny Poojary, Saman Siddiqui, Erol Staveley, Ankita Thakur, and Mohita Vyas
First Published: March 2019
Production Reference: 1260319
ISBN: 978-1-78995-639-9
Published by Packt Publishing Ltd.
Livery Place, 35 Livery Street
Birmingham B3 2PB, UK
This section briefly introduces the author, the coverage of this book, the technical skills you'll need to get started, and the hardware and software requirements required to complete all of the included activities and exercises.
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.
Alok Malik is a data scientist based in India. He has previously worked on creating and deploying unsupervised learning solutions in fields such as finance, cryptocurrency trading, logistics, and natural language processing. He has a bachelor's degree in technology from the Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, where he studied electronics and communication engineering.
Bradford Tuckfield has designed and implemented data science solutions for firms in a variety of industries. He studied math for his bachelor's degree and economics for his Ph.D. He has written for scholarly journals and the popular press, on topics including linear algebra, psychology, and public policy.
Design clever algorithms that discover hidden patterns and business-relevant insights from unstructured, unlabeled data.
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