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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. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
PREFACE - In 1912 tHERE published the first Handbook of Mining Details, the purpose and scope of which were briefly outlined in the preface as follows This book is a collection of articles that have appeared in the Engineering and Mining Jouml during th
Where does a wannabe miner or established individual operator get the information to create a small yet highly profitable mining company? When author A R C Matuska searched for simple, practical mining books and information about the industry, he found high-powered studies, academic theses and computer modeling. In short, nothing of use to the small, practical mine operator. The best information he found was in booklets aimed at ex-servicemen after World War II, encouraging them to take up mining in the British colonies in Africa. Since then, there has not been much written in such a useful and practical manner. To answer this need, a veritable goldmine of information is included in the book Practical Mining and Gold Processing for the Small Scale Operator. Where does a newcomer to the industry find out how to sample and calculate a potential resource and plan his mining business? Where does he get the information to run a small ball mill or stamp mill? How does he set up and dress a simple amalgam plate, retort some amalgam or make up a retort, and calculate the percentage of gold in bullion? Where does a small operator find out how to set up a low-cost cyanide plant and its running procedures? And how does he improve mining and blasting efficiencies? This book provides practical applications and solutions to get you started in one of the most diverse, profitable and interesting industries. It is indexed in detail so information can be easily found without sifting through realms of data. A R C Matuska is a career miner. He owns and consults for several mining properties in East and Central Africa. Publisher's website: http: //sbpra.com/ARCMatuska
This book is the first technical guide to provide a complete, generalized road map for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.