Data Mining. Practical Machine Learning Tools and Techniques

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This comprehensive guide offers a solid foundation in machine learning concepts alongside practical advice for applying these tools in real-world data mining scenarios. The third edition includes significant updates reflecting recent advancements in the field, such as new sections on Data Transformations, Ensemble Learning, Massive Data Sets, and Multi-instance Learning, as well as an updated version of the Weka machine learning software. Authors Witten, Frank, and Hall present both established techniques and cutting-edge methods, catering to a diverse audience including information systems practitioners, programmers, consultants, developers, IT managers, data analysts, and data mining professionals. It also serves as a valuable resource for professors and graduate students in data mining and machine learning courses. The book emphasizes practical tips for enhancing performance through input and output transformations in machine learning methods. Additionally, it includes access to the Weka software toolkit, featuring a range of machine learning algorithms for tasks such as data pre-processing, classification, regression, clustering, association rules, and visualization, all presented in an updated, interactive interface.