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R for Data Science

You're reading from   R for Data Science Learn and explore the fundamentals of data science with R

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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 Toomey Toomey
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Toomey
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Toc

Table of Contents (19) Chapters Close

R for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Data Mining Patterns 2. Data Mining Sequences FREE CHAPTER 3. Text Mining 4. Data Analysis – Regression Analysis 5. Data Analysis – Correlation 6. Data Analysis – Clustering 7. Data Visualization – R Graphics 8. Data Visualization – Plotting 9. Data Visualization – 3D 10. Machine Learning in Action 11. Predicting Events with Machine Learning 12. Supervised and Unsupervised Learning Index

Chapter 2. Data Mining Sequences

Data mining is frequently used to detect sequences or patterns in data. In this chapter, we are looking for the data to follow a pattern where one event or series of events predicts another data point in a consistent manner.

This chapter describes the different ways to find patterns in your dataset:

  • Patterns to look for

  • Find patterns in data

  • Constraints

We can find patterns in many large datasets. This can range across a number of areas, such as population mix changes, frequency of cell phone use, deterioration of highways, accidents due to age, and so on. It really feels like there are many patterns and sequences just waiting to be discovered.

We can find these patterns using a number of tools in R programming. Most patterns are limited in their extent by constraints, such as time over which the sequence will be meaningful.

You have been reading a chapter from
R for Data Science
Published in: Dec 2014
Publisher:
ISBN-13: 9781784390860
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