Neural-network-based models are gradually becoming the backbone of artificial intelligence and machine learning implementations. The future of data mining will be governed by the usage of artificial neural-network-based advanced modeling techniques. One obvious question: why is neural network gaining so much importance recently though it was invented in 1950s? Borrowed from the computer science domain, a neural network can be defined as a parallel information processing system where the inputs are connected with each other like neurons in the human brain to transmit information so that activities such as face recognition, image recognition, and so on can be performed. In this chapter, we are going to learn about application of neural-network-based methods in various data mining tasks such as classification, regression, time series forecasting, and feature reduction. Artificial Neural Network (ANN) functions in a...
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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Table of Contents (6 chapters)
Preface
Free Chapter
1. Module 1
2. Module 2
3. Module 3
Bibliography
Index
Customer Reviews