Data Mining with Rattle and R

The Art of Excavating Data for Knowledge Discovery

AvGraham Williams

Häftad, Engelska, 2011

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Beskrivning

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 torapidly 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.

Produktinformation

Utgivningsdatum:2011-08-04
Mått:155 x 235 x 20 mm
Vikt:612 g
Format:Häftad
Språk:Engelska
Serie:Use R!
Antal sidor:374
Upplaga:2011
Förlag:Springer-Verlag New York Inc.
ISBN:9781441998897

Utforska kategorier

Databaser inom Data och IT
Affärsapplikationer inom Data och IT

Mer om författaren

Dr Graham Williams is Senior Director of Analytics with the Australian Taxation Office, and previously Principal Computer Scientist for Data Mining with CSIRO. He is also Visiting Professor and Senior International Scientist with the Shenzhen Institutes of Advanced Analytics of the Chinese Academy of Sciences, Adjunct Professor, Data Mining, Fraud Prevention, Security, University of Canberra, and Adjunct Professor, Australian National University. Graham regularly teaches data mining courses and is author of the freely available, open source data mining system, Rattle. He has been involved in many data mining projects for clients from government and industry over his long career. His research developments included ensemble learning (1980's) and hot spots discovery (1990's). He is actively involved in the international artificial intelligence and data mining research communities, particularly as chair of the Pacific Asia Knowledge Discovery and Data Mining conference series and founder and co-chair of the Australasian Data Mining conference series. Graham has editted a number of books and authored many academic and industry papers and reports. His current focus is on making data mining technology readily accessible, ensuring research, innovation and discovery are repeatable and available, and encouraging the free and open sharing of knowledge.

Recensioner i media

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)

Innehållsförteckning

Introduction.- 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.