Data Mining and Knowledge Discovery for Geoscientists

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

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  • Author : Guangren Shi
  • Publisher : Elsevier
  • Pages : 376 pages
  • ISBN : 0124104754
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKData 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 : 09 October 2013
GET THIS BOOKData Mining and Knowledge Discovery for Geoscientists

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

Scientific Data Mining and Knowledge Discovery

Scientific Data Mining and Knowledge Discovery
  • Author : Mohamed Medhat Gaber
  • Publisher : Springer Science & Business Media
  • Release : 19 September 2009
GET THIS BOOKScientific Data Mining and Knowledge Discovery

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

Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook
  • Author : Oded Maimon,Lior Rokach
  • Publisher : Springer Science & Business Media
  • Release : 28 May 2006
GET THIS BOOKData Mining and Knowledge Discovery Handbook

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 for Big Data

Data Mining and Knowledge Discovery for Big Data
  • Author : Wesley W. Chu
  • Publisher : Springer Science & Business Media
  • Release : 24 September 2013
GET THIS BOOKData Mining and Knowledge Discovery for Big Data

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

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 : 06 December 2012
GET THIS BOOKData Mining and Knowledge Discovery for Process Monitoring and Control

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

Methodologies for Knowledge Discovery and Data Mining

Methodologies for Knowledge Discovery and Data Mining
  • Author : Ning Zhong,Lizhu Zhou
  • Publisher : Springer
  • Release : 29 June 2003
GET THIS BOOKMethodologies for Knowledge Discovery and Data Mining

This book constitutes the refereed proceedings of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD '99, held in Beijing, China, in April 1999. The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization; causal models

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Data Mining and Knowledge Discovery with Evolutionary Algorithms
  • Author : Alex A. Freitas
  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
GET THIS BOOKData Mining and Knowledge Discovery with Evolutionary Algorithms

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering

Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook
  • Author : Oded Maimon,Lior Rokach
  • Publisher : Springer Science & Business Media
  • Release : 10 September 2010
GET THIS BOOKData Mining and Knowledge Discovery Handbook

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.

Advances in Knowledge Discovery and Data Mining

Advances in Knowledge Discovery and Data Mining
  • Author : James Bailey,Latifur Khan,Takashi Washio,Gill Dobbie,Joshua Zhexue Huang,Ruili Wang
  • Publisher : Springer
  • Release : 11 April 2016
GET THIS BOOKAdvances in Knowledge Discovery and Data Mining

This two-volume set, LNAI 9651 and 9652, constitutes the thoroughly refereed proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, held in Auckland, New Zealand, in April 2016. The 91 full papers were carefully reviewed and selected from 307 submissions. They are organized in topical sections named: classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data; spatiotemporal and image data; anomaly detection and clustering; novel

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics
  • Author : Andreas Holzinger,Igor Jurisica
  • Publisher : Springer
  • Release : 17 June 2014
GET THIS BOOKInteractive Knowledge Discovery and Data Mining in Biomedical Informatics

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible

Medical Data Mining and Knowledge Discovery

Medical Data Mining and Knowledge Discovery
  • Author : J. Kacprzyk,Krzysztof J. Cios
  • Publisher : Physica
  • Release : 12 January 2001
GET THIS BOOKMedical Data Mining and Knowledge Discovery

Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge

Geographic Data Mining and Knowledge Discovery

Geographic Data Mining and Knowledge Discovery
  • Author : Harvey J. Miller,Jiawei Han,Harvey J.. Miller
  • Publisher : CRC Press
  • Release : 11 October 2001
GET THIS BOOKGeographic Data Mining and Knowledge Discovery

Advances in automated data collection are creating massive databases and a whole new field, Knowledge Discovery Databases (KDD), has emerged to develop new methods of managing and exploiting them. Geographic Data Mining and Knowledge Discovery is the interrogation of large databases using efficient computational methods. The unique challenges brought about by the storing of massive geographical databases - from high resolution satellite-based systems to data from intelligent transportation systems, for example - has led to the field of Geographical Knowledge

Mobility, Data Mining and Privacy

Mobility, Data Mining and Privacy
  • Author : Fosca Giannotti,Dino Pedreschi
  • Publisher : Springer Science & Business Media
  • Release : 12 January 2008
GET THIS BOOKMobility, Data Mining and Privacy

Mobile communications and ubiquitous computing generate large volumes of data. Mining this data can produce useful knowledge, yet individual privacy is at risk. This book investigates the various scientific and technological issues of mobility data, open problems, and roadmap. The editors manage a research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery, and this book relates their findings in 13 chapters covering all related subjects.

Data Mining with Rattle and R

Data Mining with Rattle and R
  • Author : Graham Williams
  • Publisher : Springer Science & Business Media
  • Release : 04 August 2011
GET THIS BOOKData Mining with Rattle and R

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data