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

R: Mining spatial, text, web, and social media data by [Makhabel, Bater, Mishra, Pradeepta, Danneman, Nathan, Heimann, Richard]
R: Mining spatial, text, web, and social media data
by Bater Makhabel (Author), Pradeepta Mishra (Author), Nathan Danneman (Author), Richard Heimann (Author)
Product details
Format: Kindle Edition
File Size: 38582 KB
Print pages 页数: 992 pages
Publisher Finelybook 出版社: Packt Publishing; 1 edition (19 Jun. 2017)
Language: English
ASIN: B072XN54R9

Book Description to Finelybook sorting

Create data mining algorithms
About This Book
Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms
Real-world case studies will take you from novice to intermediate to apply data mining techniques
Deploy cutting-edge sentiment analysis techniques to real-world social media data using R
Who This Book Is For
This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path.
What You Will Learn
Discover how to manipulate data in R
Get to know top classification algorithms written in R
Explore solutions written in R based on R Hadoop projects
Apply data management skills in handling large data sets
Acquire knowledge about neural network concepts and their applications in data mining
Create predictive models for classification, prediction, and recommendation
Use various libraries on R CRAN for data mining
Discover more about data potential, the pitfalls, and inferencial gotchas
Gain an insight into the concepts of supervised and unsupervised learning
Delve into exploratory data analysis
Understand the minute details of sentiment analysis
In Detail
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
Style and approach
A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.
1. Module 1
1. Warming Up
2. Mining Frequent Patterns, Associations, and Correlations
3. Classification
4. Advanced Classification
5. Cluster Analysis
6. Advanced Cluster Analysis
7. Outlier Detection
8. Mining Stream, Time-series, and Sequence Data
9. Graph Mining and Network Analysis
10. Mining Text and Web Data
2. Module 2
1. Data Manipulation Using In-built R Data
2. Exploratory Data Analysis with Automobile Data
3. Visualize Diamond Dataset
4. Regression with Automobile Data
5. Market Basket Analysis with Groceries Data
6. Clustering with E-commerce Data
7. Building a Retail Recommendation Engine
8. Dimensionality Reduction
9. Applying Neural Network to Healthcare Data
3. Module 3
1. Going Viral
2. Getting Started with R
3. Mining Twitter with R
4. Potentials and Pitfalls of Social Media Data
5. Social Media Mining – Fundamentals
6. Social Media Mining – Case Studies


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    Sherry725 5个月前 (01-10) 未知系统 回复