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Machine Learning with R Cookbook, Second Edition

You're reading from   Machine Learning with R Cookbook, Second Edition Analyze data and build predictive models

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787284395
Length 572 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Yu-Wei, Chiu (David Chiu) Yu-Wei, Chiu (David Chiu)
Author Profile Icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
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Table of Contents (15) Chapters Close

Preface 1. Practical Machine Learning with R FREE CHAPTER 2. Data Exploration with Air Quality Datasets 3. Analyzing Time Series Data 4. R and Statistics 5. Understanding Regression Analysis 6. Survival Analysis 7. Classification 1 - Tree, Lazy, and Probabilistic 8. Classification 2 - Neural Network and SVM 9. Model Evaluation 10. Ensemble Learning 11. Clustering 12. Association Analysis and Sequence Mining 13. Dimension Reduction 14. Big Data Analysis (R and Hadoop)

Introduction

Enterprises accumulate a large amount of transaction data (for example, sales orders from retailers, invoices, and shipping documentations) from daily operations. Finding hidden relationships in the data can be useful, such as What products are often bought together? or What are the subsequent purchases after buying a cell phone? To answer these two questions, we need to perform association analysis and frequent sequential pattern mining on a transaction dataset.

Association analysis is an approach to find interesting relationships within a transaction dataset. A famous association between products is customers who buy diapers also buy beer. While this association may sound unusual, if retailers can use this kind of information or rule to cross-sell products to their customers, there is a high likelihood that they can increase their sales.

Association analysis is...

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