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Wednesday, July 22, 2020 | History

3 edition of Mining modelling found in the catalog.

Mining modelling

Vlastimil Ehrenberger

Mining modelling

by Vlastimil Ehrenberger

  • 360 Want to read
  • 15 Currently reading

Published by Elsevier, Distributor for the U.S.A. and Canada, Elsevier Science Pub. Co. in Amsterdam, New York, New York, N.Y .
Written in English

    Subjects:
  • Mines and mineral resources -- Mathematical models.

  • Edition Notes

    StatementVlastimil Ehrenberger and Alfred Fajkoš.
    SeriesAdvances in mining science and technology ;, 5
    ContributionsFajkoš, Alfred.
    Classifications
    LC ClassificationsTN153 .E3713 1990
    The Physical Object
    Pagination216 p. :
    Number of Pages216
    ID Numbers
    Open LibraryOL2193210M
    ISBN 100444988602
    LC Control Number89012082

    The Gold Mine Investment Model is used to determine the financial feasibility of a proposed Gold Mine Investment. The Financial Model outputs IRR and NPV based on Key Performance Indicators (KPI)s such as ore grade, ore processed, cost per ton and other relevant variables to the mining sector.3/5(1). This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making. This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of.

    1 DATA-MINING CONCEPTS 1. Introduction 1. Data-Mining Roots 4. Data-Mining Process 6. Large Data Sets 9. Data Warehouses for Data Mining Business Aspects of Data Mining: Why a Data-Mining Project Fails Organization of This Book Review Questions and Problems References for Further Study 2. Summary. This comprehensive technical book on highwall mining covers theory and practice coupled with practical examples and design aspects. It contains eight extensive chapters elaborating broad-spectrum functionalities of highwall mining and its operational aspects, covering world scenario, economic potential, methods of coal extraction, design methodology including empirical web pillar.

    10 Free Must-Read Machine Learning E-Books For Data Scientists AI Engineers says: Febru at pm One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist. Data mining, also called knowledge discovery in databases, in computer science, the process of discovering interesting and useful patterns and relationships in large volumes of field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large digital collections, known as data sets.


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Mining modelling by Vlastimil Ehrenberger Download PDF EPUB FB2

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Pattern situations. Examples. Modelling Situations in Open-Cast Coal Mining. Modelling Situations in Uranium Deposit Mining. Sedimentary deposit working by room and pillar method. Modelling conditions of planning and control in mining a vein deposit.

Conclusions. References. Subject Index. Series Title: Advances in mining science and. Praise for Modeling with Data "Fascinating insights crop up on every page." --Scientific American Book Club "Where the author shines is his common sense and the practical tips he offers along the way.

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[V Ehrenberger; A Fajkoš] -- The main aim of this book Mining modelling book to offer an exposition of the principles and applications of an original method which was introduced by the authors, developed gradually in the course of time, and.

Get a data mining recipe for success to increase profits and reduce costs in today's business world with-This book shows you how to create and implement models of the most commonly asked data mining questions for marketing, sales, risk analysis, and customer relationship management and by: Read the latest chapters of Advances in Mining Science and Technology atElsevier’s leading platform of peer-reviewed scholarly literature Mining Modelling.

Edited by Vlastimil Ehrenberger, Book chapter Full text access 5 - Further Means. In some instances they have the character of case studies and demonstrate typical approaches applied when modelling mining situations.

The book will be of interest to a broad range of specialists working in the mining industry - research workers, designers, computer personnel, system analysts, management personnel at all managerial levels, and.

Objectives. IJDMMM aims to provide a professional forum for formulating, discussing and disseminating these solutions, which relate to the design, development, deployment, management, measurement, and adjustment of data warehousing, data mining, data modelling, data management, and other data analysis techniques.

They should form a common ground on which a data chain. Applied Predictive Modeling by Max Kuhn and Kjell Johnson is a complete examination of essential machine learning models with a clear focus on making numeric or factorial predictions. On nearly pages, the Authors discuss all topics from data engineering, modeling, and performance by:   Try Text Mining with R, as I recall it was recommended in an article by datacamp.

You can also check their interview with its author. Text Mining and Natural Language Processing in Data Science. An introductory text and reference on mining engineering highlighting the latest in mining technology Introductory Mining Engineering outlines the role of the mining engineer throughout the life of a mine, including prospecting for the deposit, determining the site's value, developing the mine, extracting the mineral values, and reclaiming the land afterward.5/5(2).

6 Topic modeling In text mining, we often have collections of documents, such as blog posts or news articles, that we’d like to divide into natural groups so that we can understand them separately. Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds natural groups of.

Chapter (PDF Available) May w Reads. How we measure 'reads' A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors. Welcome to Text Mining with R. This is the website for Text Mining with R. Visit the GitHub repository for this site, find the book at O’Reilly, or buy it on Amazon.

This work by Julia Silge and David Robinson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike United States License. mining Chile n/a n/a 7, % Grupo Mexico Diversified metals & mining Mex % 5, % Tongling Nonferrous Diversified metals & mining China 2, % 4, % KGHM Polska Miedz SA Diversified metals & mining Poland 5, % 4, % 11 Diagram 5.

Top 15 mining companies (by revenue in ) from developing File Size: 2MB. Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more; Most companies’ data mining efforts focus almost exclusively on numerical and categorical data, while text.

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mining engineers. With these factors in mind, this document has been written to introduce new mining engineers to mineral resource block models: their structure, the brands they may come across, the types they may come across, and issues that they will need to understand to avoid mistakes in their use.

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Unbounded C -netFile Size: KB.Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI.

Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for.The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naive Bayes, decision trees, and text mining.