Inductive Databases and Constraint-Based Data Mining
This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - ci
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Sašo Džeroski • Bart Goethals • Panþe Panov Editors
Inductive Databases and Constraint-Based Data Mining
1C
Editors Sašo Džeroski Jožef Stefan Institute Dept. of Knowledge Technologies Jamova cesta 39 SI-1000 Ljubljana Slovenia [email protected]
Panče Panov Jožef Stefan Institute Dept. of Knowledge Technologies Jamova cesta 39 SI-1000 Ljubljana Slovenia [email protected]
Bart Goethals University of Antwerp Mathematics and Computer Science Dept. Middelheimlaan 1 B-2020 Antwerpen Belgium [email protected]
ISBN 978-1-4419-7737-3 e-ISBN 978-1-4419-7738-0 DOI 10.1007/978-1-4419-7738-0 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010938297 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and exciting research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the unification of pattern mining approaches through constraint programming, the clarification of the relationship between mining local patterns and global models, and the proposed integrative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and knowledge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and manipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”first-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried. The IDB framework is appealing as a general framework for data mining, because it employs declarative queries instead of ad-hoc procedural constructs. As declarative queries are often formulated
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