Data mining and knowledge discovery for geoscientists

✍ Scribed by Shi, Guangren

Publisher: Elsevier
Year: 2014
Tongue: English
Leaves: 377
Category: Library

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✦ Synopsis

Introduction -- 2. Probability and statistics -- 3. Artificial neural networks -- 4. Support vector machines -- 5. Decision trees -- 6. Bayesian classification -- 7. Cluster analysis -- 8. Kriging -- 9. Other soft computing algorithms for geosciences -- 10. A practical software system of data mining and knowledge discovery for geosciences.;"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"--

✦ Table of Contents

Introduction --
2. Probability and statistics --
3. Artificial neural networks --
4. Support vector machines --
5. Decision trees --
6. Bayesian classification --
7. Cluster analysis --
8. Kriging --
9. Other soft computing algorithms for geosciences --
10. A practical software system of data mining and knowledge discovery for geosciences.

✦ Subjects

Geology--Data processing;Data mining;Geology -- Data processing

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