Description : Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Description : Measuremente and Data. Visualizing and Exploring Data. Data Analysis and Uncertainty. A Systematic Overview of Data Mining Algorithms. Models and Patterns. Score Functions for Data Mining Algorithms. Serach and Optimization Methods. Descriptive Modeling. Predictive Modeling for Classification. Predictive Modeling for Regression. Data Organization and Databases. Finding Patterns and Rules. Retrieval by Content.
Description : This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
Author by : Royal Society of Chemistry (Great Britain)
Languange : en
Publisher by : Royal Society of Chemistry
Format Available : PDF, ePub, Mobi
Total Read : 75
Total Download : 983
File Size : 50,7 Mb
Description : This first Issue in the series contains nine articles written by experts from the mining industry, regulatory authorities, and academia, and incorporates the latest research.
Description : Underground Mining Methods: Engineering Fundamentals and International Case Studies presents the latest principles and techniques in use today. Reflecting the international and diverse nature of the industry, a series of mining case studies is presented covering the commodity range from iron ore to diamonds extracted by operations located in all corners of the world. Industry experts have contributed sections on General Mine Design Considerations; Room-and-Pillar Mining of Hard Rock/Soft Rock; Longwall Mining of Hard Rock; Shrinkage Stoping; Sublevel Stoping; Cut-and-Fill Mining; Sublevel Caving; Panel Caving; Foundations for Design; and Underground Mining Looks to the Future.
Description : From its origins in the malachite mines of ancient Egypt, mining has grown to become a global industry which employs many hundreds of thousands of people. Today, the mining industry makes use of various types of complex and sophisticated equipment, for which reliability, maintainability and safety has become an important issue. Mining Equipment Reliability, Maintainability and Safety is the first book to cover these three topics in a single volume. Mining Equipment Reliability, Maintainability and Safety will be useful to a range of individuals from administrators and engineering professionals working in the mining industry to students, researchers and instructors in mining engineering, as well as design engineers and safety professionals. All topics covered in the book are treated in such a manner that the reader requires no previous knowledge to understand the contents. Examples, solutions and test problems are also included to aid reader comprehension.
Description : A perfect introduction to sustainable mining for those new to the subject or those who require some revision, this book provides a basic overview of international sustainable mining practices since 1992, with particular emphasis upon practices in the Americas, Asia and Europe. The text begins by addressing issues such as the volume of waste generated by mining, mine closure planning and the environmental impacts, and then goes into specific detail in the following areas: cleaner production practices in Australia; blasting impacts and their control in the US; minimizing surface water impacts; minimizing groundwater impacts; use of environmental indicators in mining; and emerging mining technologies that minimize environmental impacts. The text contains relevant examples and case histories for ease of revision, and also includes a chapter on Best Mining Practices for Sustainable Mining and sub-chapters on small-scale mining, tailings pond management and hazardous waste management.
Description : Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.