Data Mining and Knowledge Discovery for Geoscientists

Authored by a global thought leader in data mining, this book summarizes the latest developments and practical data application techniques for geoscientists.

Guangren Shi (Author)

9780124104372

Hardback, published 12 December 2013

376 pages
23.4 x 19 x 2.6 cm, 0.96 kg

"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

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".

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

Subject Areas: Machine learning [UYQM], Data mining [UNF], Geology & the lithosphere [RBG], Earth sciences [RB]