Data Mining and Knowledge Discovery for Geoscientists
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Data Mining and Knowledge Discovery for Geoscientists

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Description

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.
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Product details

  • Hardback | 376 pages
  • 193.04 x 238.76 x 22.86mm | 929.86g
  • United States
  • English
  • 0124104371
  • 9780124104372

Table of contents

Introduction
1 Introduction to Data Mining
2 Probability and Statistics
3 Artificial Neural Networks
4 Support Vector Machines
5 Decision Trees (DTR)
6 Bayesian Classification
7 Cluster Analysis
8 Kriging Method
9 Other Soft Computing Methods for the Geosciences
10 A Practical Data Mining and Knowledge Discovery System for the Geosciences
Index
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Review quote

"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
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