Shi G. Data Mining and Knowledge Discovery for Geoscientists

Elsevier, 2014, -372 p.This book is an aggregation of principles, methods, codes, and applications for the data mining and knowledge discovery in geosciences based on the author’s studies over the past 17 years.
In the past 20 years, the field of data mining has seen an enormous success in terms of both wide-ranging applications and scientific methodologies. Data mining is the computerized process of extracting previously unknown and important actionable information and knowledge from large databases. Such knowledge can then be used to make crucial decisions by incorporating individuals’ intuition and experience so as to objectively generate for decision makers informed options that might otherwise go undiscovered. So, data mining is also called knowledge discovery in database, and it has been widely applied in many fields of economics, science, and technology. However, data mining applications to geosciences are still at an initial stage, partly due to the multidisciplinary nature and complexity of geosciences and partly due to the fact that many new methods in data mining require time and well-tested case studies in geosciences.
Facing the challenges of large amounts of geosciences databases, geoscientists can use database management systems to conduct conventional applications (such as queries, searches, and simple statistical analysis), but they cannot obtain the available knowledge inherent in data by such methods, leading to a paradoxical scenario of rich data but poor knowledge. The true solution is to apply data mining techniques in geosciences databases and modify such techniques to suit practical applications in geosciences. This book, Data Mining and Knowledge Discovery for Geoscientists, is a timely attempt to summarize the latest developments in data mining for geosciences.
This book introduces some successful applications of data mining in geosciences in recent years for knowledge discovery in geosciences. It systematically introduces to geoscientists the widely used algorithms and discusses their basic principles, conditions of applications, and diversity of case studies as well as describing what algorithm may be suitable for a specific application.
This book focuses on eight categories of algorithm: (1) probability and statistics, (2) artificial neural networks, (3) support vector machines, (4) decision trees, (5) Bayesian classification, (6) cluster analysis, (7) Kriging method, and (8) other soft computing algorithms, including fuzzy mathematics, gray systems, fractal geometry, and linear programming.Probability and Statistics
Artificial Neural Networks
Support Vector Machines
Decision Trees
Bayesian Classification
Cluster Analysis
Kriging
Other Soft Computing Algorithms for Geosciences
A Practical Software System of Data Mining and Knowledge Discovery for Geosciences
Table of Unit Conversion∗
Answers to Exercises