Data Mining and Knowledge Discovery for Geoscientists

"In the early 21 century, data mining (DM) was predicted to be "one of the most revolutionary developments of the next decade," and chosen as one of 10 emerging technologies that will change the world (Hand et al., 2001; Larose, 2005; Larose, 2006). In fact, in the recent 20 years, the field of DM has seen enormous success, both in terms of broad-ranging application achievements and in terms of scientific progress and understanding. DM is the computerized process of extracting previously unknown and important actionable information and knowledge from database (DB). This knowledge can then be used to make crucial decisions by leveraging the individual's intuition and experience to objectively generate opportunities that might otherwise go undiscovered"--

Klappentext

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.

Zusammenfassung
Addresses challenges by summarizing the 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. This title focuses on 22 of data mining's practical algorithms and application samples.

Inhalt

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