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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

Streaming Data Clustering Analysis in R

In all those instances where data is collected from various sources, brought to a centralized location, and stored for analysis, that data is called as data at rest. There is a huge time delay between the time the data was recorded and when the analysis was performed. Analyzing the last 6 months' inventory data is an example of data at rest. Today, majority of data analysis is performed using data at rest.

With the number of Internet of Things projects on the rise, there is a great demand today to perform analysis on data in motion, also called streaming data. Streaming data is becoming ubiquitous with the number of addressable sensors and devices being added to the internet. As an example from computer network monitoring: an intrusion detection system analysis, the network packets received in real time to quickly determine if...

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