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Data Mining with Rattle and R The Art of Excavating Data for Knowledge Discovery at Meripustak

Data Mining with Rattle and R The Art of Excavating Data for Knowledge Discovery by Graham Williams , Springer

Books from same Author: Graham Williams

Books from same Publisher: Springer

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  • General Information  
    Author(s)Graham Williams
    PublisherSpringer
    ISBN9781441998897
    Pages374
    BindingPaperback
    LanguageEnglish
    Publish YearSeptember 2011

    Description

    Springer Data Mining with Rattle and R The Art of Excavating Data for Knowledge Discovery by Graham Williams

    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._x000D__x000D_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._x000D__x000D_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._x000D_ Table of contents :- _x000D_ 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._x000D_



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