Data Mining: Practical Machine Learning Tools and Techniques

This 2e of Toxoplasma gondii reflects the significant advances in the field in the last 5 years, including new information on the genomics, epigenomics and proteomics of T. gondii as well as a new understanding of the population biology and genetic diversity of this organism. T. gondii remains the best model system for studying the entire Apicomplexa group of protozoans, which includes Malaria, making this new edition essential for a broad group of researchers and scientists. Toxoplasmosis is caused by a one-celled protozoan parasite known as T. gondii. The infection produces a wide range of clinical syndromes in humans, land and sea mammals, and various bird species. Most humans contract toxoplasmosis by eating contaminated, raw or undercooked meat (particularly pork), vegetables, or milk products; by coming into contact with the T. gondii eggs from cat feces; or by drinking contaminated water. The parasite damages the ocular and central nervous systems, causing behavioral and personality alterations as well as fatal necrotizing encephalitis. It is especially dangerous for the fetus of an infected pregnant woman and for individuals with compromised immune systems, such as HIV-infected patients.Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 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. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.