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This is an introductory textbook and guide to the rapidly evolving field of predictive text mining. There are chapter summaries, historical and bibliographic remarks, and classroom-tested exercises for each chapter. Descriptive case studies are also included.
In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text.
A guide to developing data-mining applications. It offers advice on performing these large-scale, open-ended analyses for real-world data warehouses. It focuses on the preparation and organization of data and the development of an overall strategy for data mining. It reviews sophisticated prediction methods that search for patterns in big data.
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