Description
Book SynopsisData mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms.
Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing.
The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to
Trade Review
From the book reviews:
“The text does a great job of showing how to do each step using the data mining tool Rattle and related R concepts as appropriate. This makes it a great tool for someone who does not know much about R and wants to learn more about the powerful options available in R for data mining.” (Roger M. Sauter, Technometrics, Vol. 54 (3), August, 2012)
“This text is a manual for the impressive Rattle graphical user interface (GUI) for R, describing both the use of the GUI and the R code that is invoked to carry out the computations. … Data analysts … are likely to find Rattle a helpful tool that will allow them to quickly become productive with R. … There is extensive useful practical advice on data preparation and data manipulation. … is well suited for use in intermediate level courses on regression or classification.” (John H. Maindonald, International Statistical Review, Vol. 80 (1), 2012)
Table of ContentsIntroduction.- Getting Started.- Working with Data.- Loading Data.- Exploring Data.- Interactive Graphics.- Transforming Data.- Descriptive and Predictive Analytics.- Cluster Analysis.- Association Analysis.- Decision Trees.- Random Forests.- Boosting.- Support Vector Machines.- Model Performance Evaluation.- Deployment.
