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Scalable High Performance Computing For Knowledge Discovery And Data Mining 1997: Volume 1, No. 4

RRP $464.99

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Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area.
Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.


The Mining Law

RRP $713.99

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Originally published in 1987, John D. Leshy presents this scholarly study of the 1872 Mining Law as a legal treatise and history of mining in the West from the point of view of mineral exploration and production. This mining law governed the United States mining practice yet had never been changed. The Mining Law attempts to highlight the role of policy and government as well as the more obscure elements of the law which complicated mining practice in the eighties. This title will be of interest to students of Environmental Studies and policy makers.


Automating The Design Of Data Mining Algorithms

RRP $55.99

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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.



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