R Data Mining
暫譯: R 數據挖掘

Key Features

• Understand the basics of data mining and why R is a perfect tool for it.
• Manipulate your data using the popular R packages and gather valuable business insights from it.
• Written in a clear, easy to understand manner, and includes lots of practical examples involving real-world datasets

Book Description

R is widely used in leveraging data mining techniques across many different industries, including finance, medicine, scientific research and more. This book will empower you to produce and show impressive analyses from the data, selecting and implementing the appropriate data mining techniques in R.

The book begins with a detailed introduction to data mining and why R is a popular alternative for it. You will get a comprehensive coverage of the various R packages which you can use in the data mining process. We will then proceed to use these packages for manipulating various datasets, through practical examples including real-world datasets. Implement algorithms like k-means, SVM, and more, and techniques like classification and cluster analysis to extract insightful patterns and associations. Topics like outlier detection, regression analysis, anomaly detection and network analysis are also covered, in a very easy to understand manner. You will also use the popular ggplot2 package to visualize the insights you get from the analysis, and aid your decision-making.

By the end of this book, you will have grasped the fundamentals of data mining, and the various techniques you can deploy with the popular R packages to get the most out of your data.

What you will learn

• Get introduced to most relevant packages for data mining within the R environment.
• Get confident about data quality and structure through data validation and exploratory data analysis
• Learn relevant steps to validate all performed analysis
• Develop a regression model from your real gmail data
• Produce clear and effective reports to show analyses results
• Get insights from your analyses using meaningful visualizations with ggplot2