High-Utility Pattern Mining Theory, Algorithms and Applications
This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining,
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Philippe Fournier-Viger Jerry Chun-Wei Lin Roger Nkambou Bay Vo Vincent S. Tseng Editors
High-Utility Pattern Mining Theory, Algorithms and Applications
Studies in Big Data Volume 51
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI Web of Science, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink.
More information about this series at http://www.springer.com/series/11970
Philippe Fournier-Viger Jerry Chun-Wei Lin Roger Nkambou Bay Vo Vincent S. Tseng •
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Editors
High-Utility Pattern Mining Theory, Algorithms and Applications
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Editors Philippe Fournier-Viger Harbin Institute of Technology (Shenzhen) Shenzhen, China Jerry Chun-Wei Lin Western Norway University of Applied Sciences Bergen, Norway
Bay Vo Ho Chi Minh City University of Technology Ho Chi Minh City, Vietnam Vincent S. Tseng National Chiao Tung University Hsinchu, Taiwan
Roger Nkambou Université du Québec à Montréal Montreal, QC, Canada
ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-030-04920-1 ISBN 978-3-030-04921-8 (eBook) https://doi.org/10.1007/978-3-030-04921-8 Library of Congress Control Number: 2018962757 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names,
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