November 26, 2020

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.

Geographic Data Mining and Knowledge Discovery, Second Edition

Geographic Data Mining and Knowledge Discovery, Second Edition
Author : Harvey J. Miller,Jiawei Han
Publisher : CRC Press
Release Date : 2009-05-27
Category : Computers
Total pages :486
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The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal Databases Since the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (GVis) have been developed. In addition, there has been a rise in the use of knowledge discovery techniques due to the increasing collection and storage of data on spatiotemporal processes and mobile objects. Incorporating these novel developments, this second edition reflects the current state of the art in the field. New to the Second Edition Updated material on geographic knowledge discovery (GKD), GDW research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, the INGENS 2.0 software, and GVis techniques New chapter on data quality issues in GKD New chapter that presents a tree-based partition querying methodology for medoid computation in large spatial databases New chapter that discusses the use of geographically weighted regression as an exploratory technique New chapter that gives an integrated approach to multivariate analysis and geovisualization Five new chapters on knowledge discovery from spatiotemporal and mobile objects databases Geographic data mining and knowledge discovery is a promising young discipline with many challenging research problems. This book shows that this area represents an important direction in the development of a new generation of spatial analysis tools for data-rich environments. Exploring various problems and possible solutions, it will motivate researchers to develop new methods and applications in this emerging field.

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 for Process Monitoring and Control

Data Mining and Knowledge Discovery for Process Monitoring and Control
Author : Xue Z. Wang
Publisher : Springer Science & Business Media
Release Date : 2012-12-06
Category : Computers
Total pages :251
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Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.

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 : Elsevier
Release Date : 2020-04-10
Category : Science
Total pages :472
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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. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook
Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Release Date : 2010-09-10
Category : Computers
Total pages :1285
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This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.

Advanced Techniques in Knowledge Discovery and Data Mining

Advanced Techniques in Knowledge Discovery and Data Mining
Author : Nikhil Pal
Publisher : Springer Science & Business Media
Release Date : 2007-12-31
Category : Computers
Total pages :256
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Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.

Knowledge Discovery from Legal Databases

Knowledge Discovery from Legal Databases
Author : Andrew Stranieri,John Zeleznikow
Publisher : Springer Science & Business Media
Release Date : 2006-03-30
Category : Computers
Total pages :298
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Knowledge Discovery from Legal Databases is the first text to describe data mining techniques as they apply to law. Law students, legal academics and applied information technology specialists are guided thorough all phases of the knowledge discovery from databases process with clear explanations of numerous data mining algorithms including rule induction, neural networks and association rules. Throughout the text, assumptions that make data mining in law quite different to mining other data are made explicit. Issues such as the selection of commonplace cases, the use of discretion as a form of open texture, transformation using argumentation concepts and evaluation and deployment approaches are discussed at length.

Data Mining and Knowledge Discovery for Big Data

Data Mining and Knowledge Discovery for Big Data
Author : Wesley W. Chu
Publisher : Springer Science & Business Media
Release Date : 2013-09-24
Category : Computers
Total pages :311
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The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

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.

Trends and Applications in Knowledge Discovery and Data Mining

Trends and Applications in Knowledge Discovery and Data Mining
Author : Leong Hou U.,Hady W. Lauw
Publisher : Springer Nature
Release Date : 2019-09-11
Category : Computers
Total pages :366
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This book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, in Macau, China, in April 2019. The 31 revised papers presented were carefully reviewed and selected from a total of 52 submissions. They stem from the following workshops: · PAISI 2019: 14th Pacific Asia Workshop on Intelligence and Security Informatics · WeL 2019: PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future · LDRC 2019: PAKDD 2019 Workshop on Learning Data Representation for Clustering · BDM 2019: 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining · DLKT 2019: 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer

Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook
Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Release Date : 2006-05-28
Category : Computers
Total pages :1383
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Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Data Mining and Knowledge Discovery in Real Life Applications

Data Mining and Knowledge Discovery in Real Life Applications
Author : Julio Ponce,Adem Karahoca
Publisher : BoD – Books on Demand
Release Date : 2009-01-01
Category : Computers
Total pages :402
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This book presents four different ways of theoretical and practical advances and applications of data mining in different promising areas like Industrialist, Biological, and Social. Twenty six chapters cover different special topics with proposed novel ideas. Each chapter gives an overview of the subjects and some of the chapters have cases with offered data mining solutions. We hope that this book will be a useful aid in showing a right way for the students, researchers and practitioners in their studies.