June 12, 2021

Download Ebook Free Data Mining And Knowledge Discovery For Geoscientists

Data Mining and Knowledge Discovery for Geoscientists

Data Mining and Knowledge Discovery for Geoscientists
Author : Guangren Shi
Publisher : Elsevier
Release Date : 2013-10-09
Category : Computers
Total pages :376
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Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists have no practical knowledge or experience using data mining techniques. For the few that do, they typically lack expertise in using data mining software and in selecting the most appropriate algorithms for a given application. This leads to a paradoxical scenario of "rich data but poor knowledge". The true solution is to apply data mining techniques in geosciences databases and to modify these techniques for practical applications. Authored by a global thought leader in data mining, Data Mining and Knowledge Discovery for Geoscientists addresses these challenges by summarizing the latest developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information. Focuses on 22 of data mining’s most practical algorithms and popular application samples Features 36 case studies and end-of-chapter exercises unique to the geosciences to underscore key data mining applications Presents a practical and integrated system of data mining and knowledge discovery for geoscientists Rigorous yet broadly accessible to geoscientists, engineers, researchers and programmers in data mining Introduces widely used algorithms, their basic principles and conditions of applications, diverse case studies, and suggests algorithms that may be suitable for specific applications

Scientific Data Mining and Knowledge Discovery

Scientific Data Mining and Knowledge Discovery
Author : Mohamed Medhat Gaber
Publisher : Springer Science & Business Media
Release Date : 2009-09-19
Category : Computers
Total pages :400
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Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.

Spatio-Temporal Data Mining and Knowledge Discovery: Issues Overview

Spatio-Temporal Data Mining and Knowledge Discovery: Issues Overview
Author : Anonim
Publisher : Unknown
Release Date : 2002
Category :
Total pages :21
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Data mining or knowledge discovery refers to a variety of techniques having the intent of uncovering useful patterns and associations from large databases. The initial steps of data mining are concerned with preparation of data, including data cleaning intended to resolve errors and missing data and integration of data from multiple heterogeneous sources. Next are the steps needed to prepare for actual data mining including the selection of the specific data relevant to the task and the transformation of this data into a format required by the data mining approach. Finally specific data mining algorithms such as class description, association rules and classification clustering are applied. There are specific characteristics of spatial and temporal data, as found in GIS and multi%media data, that make knowledge discovery in this domain more complex than in mining ordinary data such as found in typical business sales applications. Here we provide a survey of work in spatio-temporal data mining emphasizing the special characteristics. An overview is given of different sources and types of geospatial, oceanographic and meteorological data and the associated issues inherent in their use in knowledge discovery.

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publisher : CRC Press
Release Date : 2017-08-01
Category : Computers
Total pages :208
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From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Geoinformatics

Geoinformatics
Author : A. Krishna Sinha
Publisher : Geological Society of America
Release Date : 2006-01-01
Category : Science
Total pages :282
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"The science of informatics in the broadest sense has been several thousands of years in the making. With the recent emergence of large storage devices and high-speed processing of data, it has become possible to organize vast amounts of data as digital products with ontologic tags and concepts for smart queries. Coupling this computational capability with earth science data defines the emerging field of geoinformatics. Since the science of geology was established several centuries ago, observations led to conclusions that were integrative in concept and clearly had profound implications for the birth of geology. As disciplinary information about Earth becomes more voluminous, the use of geoinformatics will lead to integrative, science-based discoveries of new knowledge about planetary systems. Twenty one research papers, co-authored by 96 researchers from both earth and computer sciences, provide the first-ever organized presentation of the science of informatics as it relates to geology. Readers will readily recognize the vast intellectual content represented by these papers as they seek to address the core research goals of geoinformatics."--Publisher's website.

Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery
Author : Anonim
Publisher : Unknown
Release Date : 2003
Category : Data mining
Total pages :129
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Cartography and Geographic Information Science

Cartography and Geographic Information Science
Author : Anonim
Publisher : Unknown
Release Date : 2005
Category : Cartography
Total pages :129
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Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author : Petr Skoda,Fathalrahman Adam
Publisher : Unknown
Release Date : 2020-03
Category :
Total pages :400
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Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.

Remote Sensing

Remote Sensing
Author : Gobron N.,E. J. Llewellyn
Publisher : Unknown
Release Date : 2008
Category : Remote sensing
Total pages :231
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Geoinformatics

Geoinformatics
Author : Geoscience Information Society. Meeting
Publisher : Unknown
Release Date : 2005
Category : Earth sciences
Total pages :125
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TELSIKS 2003

TELSIKS 2003
Author : Anonim
Publisher : Unknown
Release Date : 2003
Category :
Total pages :436
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On Geographic Knowledge Discovery

On Geographic Knowledge Discovery
Author : Feng Qi
Publisher : Unknown
Release Date : 2005
Category :
Total pages :225
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Artificial Intelligent Approaches in Petroleum Geosciences

Artificial Intelligent Approaches in Petroleum Geosciences
Author : Constantin Cranganu,Henri Luchian,Mihaela Elena Breaban
Publisher : Springer
Release Date : 2015-04-20
Category : Technology & Engineering
Total pages :290
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This book presents several intelligent approaches for tackling and solving challenging practical problems facing those in the petroleum geosciences and petroleum industry. Written by experienced academics, this book offers state-of-the-art working examples and provides the reader with exposure to the latest developments in the field of intelligent methods applied to oil and gas research, exploration and production. It also analyzes the strengths and weaknesses of each method presented using benchmarking, whilst also emphasizing essential parameters such as robustness, accuracy, speed of convergence, computer time, overlearning and the role of normalization. The intelligent approaches presented include artificial neural networks, fuzzy logic, active learning method, genetic algorithms and support vector machines, amongst others. Integration, handling data of immense size and uncertainty, and dealing with risk management are among crucial issues in petroleum geosciences. The problems we have to solve in this domain are becoming too complex to rely on a single discipline for effective solutions and the costs associated with poor predictions (e.g. dry holes) increase. Therefore, there is a need to establish a new approach aimed at proper integration of disciplines (such as petroleum engineering, geology, geophysics and geochemistry), data fusion, risk reduction and uncertainty management. These intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and mining, data analysis and interpretation, and knowledge discovery, from diverse data such as 3-D seismic, geological data, well logging, and production data. This book is intended for petroleum scientists, data miners, data scientists and professionals and post-graduate students involved in petroleum industry.

Data Mining Applications for Empowering Knowledge Societies

Data Mining Applications for Empowering Knowledge Societies
Author : Hakikur Rahman
Publisher : IGI Global
Release Date : 2009
Category : Business & Economics
Total pages :332
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"This book presents an overview on the main issues of data mining, including its classification, regression, clustering, and ethical issues"--Provided by publisher.

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
Author : Ashok N Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publisher : CRC Press
Release Date : 2020-07-02
Category :
Total pages :226
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From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.