The book R Data Mining Blueprints focuses mainly on learning methods and steps in performing data mining using the R programming language as a platform. Since R is an open source tool, learning data mining using R is very interesting for learners at all levels. The book is designed in such a way that the user can start from data management techniques, exploratory data analysis, data visualization, and modeling up to creating advanced predictive modeling such as recommendation engines, neural network models, and so on. This chapter gives an overview of the concept of data mining, its various facets with data science, analytics, statistical modeling, and visualization. This chapter gives a glimpse of programming basics using R, how to read and write data, programming notations, and syntax understanding with the help of a real-world case study. This chapter includes R scripts for practice to get hands-on experience of the concepts...
R: Mining spatial, text, web, and social media data
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R: Mining spatial, text, web, and social media data
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Overview of this book
Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.
You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects.
Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects.
After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
? Learning Data Mining with R by Bater Makhabel
? R Data Mining Blueprints by Pradeepta Mishra
? Social Media Mining with R by Nathan Danneman and Richard Heimann
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R: Mining spatial, text, web, and social media data
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Table of Contents (6 chapters)
Preface
Free Chapter
1. Module 1
2. Module 2
3. Module 3
Bibliography
Index
Customer Reviews