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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 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

Introducing the stream package


Package stream is based on two major components:

  1. Data stream data is used to connect to data streams.
  2. Data stream task is to used to perform a data mining task on the data stream.

It's an extensible framework to work on data in motion.

Let us quickly look at the major components inside this framework:

Let us look at the individual boxes in the subsequent sections.

Data stream data

Data stream data (DSD) is an abstraction layer which connects to any streaming data source (of course, with some small hacks, which we will see as we progress). The stream package provides several DSD implementations.

Let us look at them:

DSD as a static simulator

As a simulator DSD can simulate static streams as well as streams with drift. In cases where we are developing algorithms to work on streaming data, we can use this simulator feature effectively.

Let us see how DSD can be leveraged as a data simulator:

> library(stream, quietly = TRUE)
> set.seed(100)
> gaussian.stream &lt...
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