Data Mining Book Review: Data Mining with Rattle and R
Do you use an open source language for data mining? If yes, then you certainly use R. The language is powerful but not straightforward to learn. The good news is that Graham Williams has the solution for you: Data Mining with Rattle and R – The Art of Excavating Data for Knowledge Discovery.
The book starts with an introduction to data mining and R. Rattle is a graphical interface for data mining using R. Although Rattle is introduced and screenshots appear in the text, it’s not needed to use the tool to benefits from the book. To be noted that all pictures are in colors. In addition to standard data mining algorithms (decision tree, association rules, SVM, etc.), Graham discusses topics such as data preparation, model evaluation and deployment (PMML).
One really good thing about Graham’s book is that each choice is explained. From data separation to algorithm tuning, everything is justified. It’s appreciated to read comprehensive theory before digging into examples and codes. The book is an excellent step by step tutorial with all codes needed for your projects. Each chapter is concluded with a summary of the R commands used.
If you are using R or plan to use R for data mining, you should definitely have this book with you. You may already know or have the book by Luis Torgo, Data Mining with R. These books are complementary. Graham’s book is a strong starting point for learning data mining using R. The book of Luis is an excellent continuation, full of case studies and explanations on how to avoid data mining pitfalls.