This book, the result of a congressionally mandated study, examines the adequacy of the regulatory framework for mining of hardrock mineralsâ€"such as gold, silver, copper, and uraniumâ€"on over 350 million acres of federal lands in the western United States. These lands are managed by two agenciesâ€"the Bureau of Land Management in the Department of the Interior, and the Forest Service in the Department of Agriculture. The committee concludes that the complex network of state and federal laws that regulate hardrock mining on federal lands is generally effective in providing environmental protection, but improvements are needed in the way the laws are implemented and some regulatory gaps need to be addressed. The book makes specific recommendations for improvement, including: The development of an enhanced information management system and a more efficient process to review new mining proposals and issue permits. Changes to regulations that would require all mining operations, other than "casual use" activities that negligibly disturb the environment, to provide financial assurances for eventual site cleanup. Changes to regulations that would require all mining and milling operations (other than casual use) to submit operating plans in advance.

In this second edition of his in-depth and gripping account of the Arizona Miners' Strike of 1983, Jonathan D. Rosenblum describes in a new epilogue the resurgence of union activism at Steelworkers Local 890 in Silver City, New Mexico, more than a decade since the devastating campaign waged by the Phelps Dodge Corporation to obliterate the unions at its Arizona properties.

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

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