Mining Multiple Large Databases
Effective data analysis using multiple databases requires highly accurate patterns. As the local pattern analysis might extract patterns of low quality from multiple databases, it becomes necessary to improve mining multiple databases. In this chapter, we
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Animesh Adhikari · Pralhad Ramachandrarao · Witold Pedrycz
Developing Multi-database Mining Applications
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Animesh Adhikari Department of Computer Science Smt. Parvatibal Chowgule College Margoa-403602 India [email protected]
Pralhad Ramachandrarao Department of Computer Science & Technology Goa University Goa-403206 India [email protected]
Witold Pedrycz Department of Electrical & Computer Engineering University of Alberta 9107 116 Street Edmonton AB T6G 2V4 Canada [email protected]
AI&KP ISSN 1610-3947 ISBN 978-1-84996-043-4 e-ISBN 978-1-84996-044-1 DOI 10.1007/978-1-84996-044-1 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2010922804 © Springer-Verlag London Limited 2010 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of 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 laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To Jhimli and Sohom
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . 1.1 Motivation . . . . . . . . . . . . . . . . . . . . 1.2 Distributed Data Mining . . . . . . . . . . . . 1.3 Existing Multi-database Mining Approaches . . 1.3.1 Local Pattern Analysis . . . . . . . . . 1.3.2 Sampling . . . . . . . . . . . . . . . . 1.3.3 Re-mining . . . . . . . . . . . . . . . . 1.4 Applications of Multi-database Mining . . . . . 1.5 Improving Multi-database Mining . . . . . . . 1.5.1 Various Issues of Developing Effective Multi-database Mining Applications . . 1.6 Experimental Settings . . . . . . . . . . . . . . 1.7 Future Directions . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . .
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