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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 (15) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Association Rule Mining FREE CHAPTER 2. Fuzzy Logic Induced Content-Based Recommendation 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

Chapter 6. Record Linkage - Stochastic and Machine Learning Approaches

In a large database of records, synonymous records pose a great problem. Two records referring to the same entity are considered to be synonymous. In the absence of a common identifier, such as a primary key or foreign key, joining such records based on the entities is a tough task. Let's illustrate this with a quick example. Consider the following two records:

Sno

First name

Middle name

Last name

Address

City

State

Zip

1

John

NULL

NULL

312 Delray Ave

Deer Field

FL

33433

2

John

NULL

Sanders

312 Delray Beach Ave

Deer Field

FL

33433

 

Both the records refer to the same entity, one Mr. John. Record linkage refers to an umbrella of algorithms that are designed to solve the exact same problem. Record linkage plays a key role today in various applications such as CRM, Loyalty to name a few. They are an integral part of today's sophisticated business intelligence systems and master data management systems.

Note

Disabled Airplane Pilots – a successful application...

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