Data Mining : Practical Machine Learning Tools and Techniques with Java Implementations

Witten, Ian H., Frank, Eibe

ISBN 10: 1558605525 / ISBN 13: 9781558605527

Editore: Elsevier Science & Technology, 1999

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Ships from the UK. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects. Codice articolo GRP70507640

Dati bibliografici

Titolo: Data Mining : Practical Machine Learning ...

Casa editrice: Elsevier Science & Technology

Data di pubblicazione: 1999

Legatura: Brossura

Condizione: Very Good

Edizione: 1st.

Descrizione articolo

Riassunto:

This book offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining―including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource.

Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.

* Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques.
* Covers performance improvement techniques, including input preprocessing and combining output from different methods.
* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.

Recensione:

"This is a milestone in the synthesis of data mining, data analysis, information theory, and machine learning."
—Jim Gray, Microsoft Research