Book Image

R: Mining spatial, text, web, and social media data

By : Nathan H. Danneman, Richard Heimann, Pradeepta Mishra, Bater Makhabel
Book Image

R: Mining spatial, text, web, and social media data

By: Nathan H. Danneman, Richard Heimann, Pradeepta Mishra, Bater Makhabel

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
Table of Contents (6 chapters)

What this learning path covers

Module 1, Learning Data mining with R, will teach you how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations 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 RHadoop projects. You will finish this module by feeling confident in your ability to know which data mining algorithm to apply in any situation.

Module 2, R Data Mining Blueprints, explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. You will learn about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. This module will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.

Module 3, Social Media Mining with R, begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.