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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Toc

Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining 2. Fuzzy Logic Induced Content-Based Recommendation FREE CHAPTER 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Summary


We started this chapter by explaining our problem and dataset and conducted product categorization using network/graph theory. We introduced the R package, igraph, to create and manipulate graphs. We set out discovering some of the properties of the graph and also a little bit about the graphs and random walks. We used the arules package to find frequent pairs of items and finally applied the graph clustering algorithm to our data to discover product categories. We were also able to identify important vertices performing the role of the core and connectors.

 

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