Data Mining and Knowledge Discovery for Geoscientists

✍ Scribed by Guangren Shi (Auth.)

Publisher: Elsevier
Year: 2014
Tongue: English
Leaves: 371
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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

✦ Table of Contents

Content:
Front Matter, Page iii
Copyright, Page iv
Preface, Pages vii-viii
Chapter 1 - Introduction, Pages 1-22
Chapter 2 - Probability and Statistics, Pages 23-53
Chapter 3 - Artificial Neural Networks, Pages 54-86
Chapter 4 - Support Vector Machines, Pages 87-110
Chapter 5 - Decision Trees, Pages 111-138
Chapter 6 - Bayesian Classification, Pages 139-190
Chapter 7 - Cluster Analysis, Pages 191-237
Chapter 8 - Kriging, Pages 238-274
Chapter 9 - Other Soft Computing Algorithms for Geosciences, Pages 275-319
Chapter 10 - A Practical Software System of Data Mining and Knowledge Discovery for Geosciences, Pages 320-340
Appendix 1 - Table of Unit Conversion, Pages 341-344
Appendix 2 - Answers to Exercises, Pages 345-360
Index, Pages 361-367

📜 SIMILAR VOLUMES

Data mining and knowledge discovery for

📁 Data mining and knowledge discovery for geoscientists

✍ Shi, Guangren 📂 Library 📅 2014 🏛 Elsevier 🌐 English

1. Introduction -- 2. Probability and statistics -- 3. Artificial neural networks -- 4. Support vector machines -- 5. Decision trees -- 6. Bayesian classification -- 7. Cluster analysis -- 8. Kriging -- 9. Other soft computing algorithms for geosciences -- 10. A practical software system of data min

Data Mining Methods for Knowledge Discov

📁 Data Mining Methods for Knowledge Discovery

✍ Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski (auth.) 📂 Library 📅 1998 🏛 Springer US 🌐 English

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, ge

Mathematical Methods for Knowledge Disco

📁 Mathematical Methods for Knowledge Discovery and Data Mining

✍ Giovanni Felici, Carlo Vercellis 📂 Library 📅 2007 🌐 English

The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques

Soft Computing for Knowledge Discovery a

📁 Soft Computing for Knowledge Discovery and Data Mining

✍ Oded Maimon, Lior Rokach 📂 Library 📅 2007 🏛 Springer 🌐 English

Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the env

Feature Selection for Knowledge Discover

📁 Feature Selection for Knowledge Discovery and Data Mining

✍ Huan Liu, Hiroshi Motoda (auth.) 📂 Library 📅 1998 🏛 Springer US 🌐 English

As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data colle

Soft Computing for Knowledge Discovery a

📁 Soft Computing for Knowledge Discovery and Data Mining

✍ Oded Maimon, Lior Rokach (auth.), Oded Maimon, Lior Rokach (eds.) 📂 Library 📅 2008 🏛 Springer US 🌐 English

Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful and insightful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery an