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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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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

Product network analysis


There are two steps in product network analysis. The first step is to transform the point-of-sale data into product pairs and their transaction frequency. The second step is to create a graph using data from the first step and run a clustering algorithm on the graph. The subgraphs or the clusters formed are presented as the micro-categories. Also, some products in the graph play key roles. Clustering and visualizing these product subgraphs will also help us identify those key products. According to the white paper by Corte Consultancy, a product fitting any of the following definitions is considered as key to the network:

  • The core product: In a subgraph or a cluster group, the product that is most commonly purchased in the group is termed as the core product of that group.
  • The connectors: These are products that connect two subgraphs or clusters together. They are the ones that are typically bought first, if a customer starts shopping for products in that subgraph...
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