Fr. 157.00

Data Mining and Knowledge Discovery for Geoscientists

English · Hardback

Shipping usually within 3 to 5 weeks

Description

Read more

"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"--

List of contents

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

Report

"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

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.