Data Mining and Knowledge Discovery for Geoscientists

ISBN-13: 9780124104372 / Angielski / Twarda / 2013 / 376 str.

Guangren Shi

Opis

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

Recenzje

"Shi introduces geological scientists to algorithms that are widely used for data mining and knowledge discovery, describes how they have been and could be applied in the geosciences, and surveys some successful applications. The algorithms fall into the categories of probability and statistics, artificial neural networks, support vector machines, decision trees, Bayesian classification, cluster analysis, the Kriging method, and fuzzy mathematics."-ProtoView.com, February 2014

Spis treści

Introduction 1 Introduction to Data Mining2 Probability and Statistics 3 Artificial Neural Networks 4 Support Vector Machines5 Decision Trees (DTR)6 Bayesian Classification7 Cluster Analysis 8 Kriging Method 9 Other Soft Computing Methods for the Geosciences 10 A Practical Data Mining and Knowledge Discovery System for the GeosciencesIndex