Learn about data mining with real-world datasets
About This Book
- Diverse real-world datasets to teach data mining techniques
- Practical and focused on real-world data mining cases, this book covers concepts such as spatial data mining, text mining, social media mining, and web mining
- Real-world case studies illustrate various data mining techniques, taking you from novice to intermediate
Who This Book Is For
Data analysts from beginner to intermediate level who need a step-by-step helping hand in developing complex data mining projects are the ideal audience for this book. They should have prior knowledge of basic statistics and little bit of programming language experience in any tool or platform.
What You Will Learn
- Make use of statistics and programming to learn data mining concepts and its applications
- Use R Programming to apply statistical models on data
- Create predictive models to be applied for performing classification, prediction and recommendation
- Use of various libraries available on R CRAN (comprehensive R archivesnetwork) in data mining
- Apply data management steps in handling large datasets
- Learn various data visualization libraries available in R for representing data
- Implement various dimension reduction techniques to handle large datasets
- Acquire knowledge about neuralnetwork concept drawn from computer science and its applications in data mining
The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to produce data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users.
This book 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. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.
Style and approach
This fast-paced guide will help you solve predictive modeling problems using the most popular data mining algorithms through simple, practical cases.
Table of Contents
Chapter 1: Data Manipulation Using In-built R Data
Chapter 2: Exploratory Data Analysis with Automobile Data
Chapter 3: Visualize Diamond Dataset
Chapter 4: Regression with Automobile Data
Chapter 5: Market Basket Analysis with Groceries Data
Chapter 6: Clustering with E-commerce Data
Chapter 7: Building a Retail Recommendation Engine
Chapter 8: Dimensionality Reduction
Chapter 9: Applying NeuralNet
work to Healthcare Data