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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. + Authors, Ian Witten and Eibe Frank, recipients of the 2005 ACM SIGKDD Service Award. + Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods; + Performance improvement techniques that work by transforming the input or output; + Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface.

Data mining --- 681.3*H28 --- 681.3*I26 --- 681.3*H2 --- 681.3*H3 --- 681.3*J3 --- Management Information System --- dataverwerking --- econometrie --- wiskundige statistiek --- 006.3 --- Java (Computer program language) --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- 681.3*J3 Life and medical sciences (Computer applications) --- Life and medical sciences (Computer applications) --- Object-oriented programming languages --- JavaSpaces technology --- 681.3*H28 Database applications --- Database applications --- 681.3*H2 Database management: security; integrity; protection--See also {?681.5*E5} --- Database management: security; integrity; protection--See also {?681.5*E5} --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Information storage and retrieval --- Data mining. --- Agrotechnology and Food Sciences. Information and Communication Technology --- Data Processing, Database Management. --- Artificial intelligence. Robotics. Simulation. Graphics

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Data mining

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Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques 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 today as well as methods at the leading edge of contemporary research. Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; New chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material. * Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques * Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods * Performance improvement techniques that work by transforming the input or output * Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive Interface.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Data mining. --- Data mining --- 301 --- AA / International- internationaal --- 681.3*H28 --- 681.3*H2 --- 681.3*H3 --- 681.3*I26 --- 681.3*J3 --- Techniek van statistische inlichtingen. Organisatie van de statistische enquêtes. Statistische kritiek --- Information storage and retrieval --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- 681.3*J3 Life and medical sciences (Computer applications) --- Life and medical sciences (Computer applications) --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*H2 Database management: security; integrity; protection--See also {?681.5*E5} --- Database management: security; integrity; protection--See also {?681.5*E5} --- 681.3*H28 Database applications --- Database applications

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Data Mining: Practical Machine Learning Tools and Techniques 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.

Artificial intelligence. Robotics. Simulation. Graphics