Scientific Data Mining and Knowledge Discovery Principles and Founda
With the evolution in data storage, large databases have stimulated researchers from many areas, especially machine learning and statistics, to adopt and develop new techniques for data analysis in different fields of science. In particular, there have be
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Mohamed Medhat Gaber Editor
Scientific Data Mining and Knowledge Discovery Principles and Foundations
ABC
Editor Mohamed Medhat Gaber Caulfield School of Information Technology Monash University 900 Dandenong Rd. Caulfield East, VIC 3145 Australia [email protected]
Color images of this book you can find on www.springer.com/978-3-642-02787-1 ISBN 978-3-642-02787-1 e-ISBN 978-3-642-02788-8 DOI 10.1007/978-3-642-02788-8 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2009931328 ACM Computing Classification (1998): I.5, I.2, G.3, H.3 c Springer-Verlag Berlin Heidelberg 2010 ° This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: KuenkelLopka GmbH Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
This book is dedicated to: My parents: Dr. Medhat Gaber and Mrs. Mervat Hassan My wife: Dr. Nesreen Hassaan My children: Abdul-Rahman and Mariam
Contents
Introduction . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . Mohamed Medhat Gaber
1
Part I Background Machine Learning .. . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . Achim Hoffmann and Ashesh Mahidadia
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Statistical Inference . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 53 Shahjahan Khan The Philosophy of Science and its relation to Machine Learning . .. . . . . . . . . . . 77 Jon Williamson Concept Formation in Scientific Knowledge Discovery from a Constructivist View. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 91 Wei Peng and John S. Gero Knowledge Representation and Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .111 Stephan Grimm Part II Computational Science Spatial Techniques . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .141 Nafaa Jabeur and Nabil Sahli Computational Chemistry.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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