Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.
"This book provides a general description of international sustainable mining practices since 1992 and formulates a standard definition of sustainable mining. It gives a detailed overview of the current status of mining practices in the Americas, Asia (with an emphasis on India), and Europe. Issues in sustainable mining practices addressed in this book include: the large volume of waste generated during mining; mine closure planning; managing the environmental impacts of mining; land use planning; and energy use management. The exclusive specialty of this book is entrenched in the detailed coverage of the sustainable mining systems and technologies that are currently used in developed countries." "Mineland reclamation and the role of environmental indicators in mining operations are discussed in great detail, with several examples of successful mineland reclamation in the U.S., and abandoned mineland reclamation in the western U.S." "A chapter is devoted to Best Mining Practices for Sustainable Mining and the concluding chapter of the book presents several case histories of sustainable mining practices in the Americas (including sustainable exploration practices), Asia (with emphasis on India) and Africa (Tanzania and Zambia)."--BOOK JACKET.
Knowledge, hidden in voluminous data repositories routinely created and maintained by today s applications, can be extracted by data mining. The next step is to transform this discovered knowledge into the inference mechanisms or simply the behavior of agents and multi-agent systems. Agent Intelligence Through Data Mining addresses this issue, as well as the arguable challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity. This book contains a methodology, tools and techniques, and several examples of agent-based applications developed with this approach. This volume focuses mainly on the use of data mining for smarter, more efficient agents.
Agent Intelligence Through Data Mining is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science."
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