Data Mining with Rattle and R : The Art of Excavating Data for Knowledge Discovery - Graham Williams

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Data Mining with Rattle and R

The Art of Excavating Data for Knowledge Discovery

By: Graham Williams

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Paperback | 4 August 2011

At a Glance

Paperback
396 Pages

Dimensions(cm)
22.23 x 15.24 x 1.91

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$119.90

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Data 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 rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Industry Reviews

From the reviews: "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)

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