Mining Sequential Patterns from Large Data Sets (Advances in Database Systems, Band 28) - Softcover

Wang, Wei; Yang, Jiong

Softcover

ISBN 10: 1441937072 ISBN 13: 9781441937070

Verlag: Springer, 2010

Inhaltsangabe

In many applications, such as bioinformatics, web access traces, and system utilization logs, the data is naturally in the form of sequences. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. To meet the different needs of various applications, several models of sequential patterns have been proposed. This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. It provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable.

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Über die Autorin bzw. den Autor

Dr. Chen Gao got his B.E. degree in Flight Vehicle Design and Engineering from Northwestern Polytechnical University (China) in 2015, and Ph.D. degrees in Aerospace Science and Technology from Northwestern Polytechnical University (China) and Polytechnic University of Catalonia (Spain), respectively, in 2022. He is now an Associated Professor at Northwestern Polytechnical University (China) and has been working on different topics of Astrodynamics, including station keeping, solar sailing and formation flying. Dr. Wei Wang received his B.S., M.S. and Ph.D. degrees in Aerospace Engineering from Northwestern Polytechnical University, China, in 2010, 2013 and 2018, respectively. From 2015 to 2017, he has been a visiting scholar in the Department of Civil and Industrial Engineering of the University of Pisa. He is currently an Associate Professor with the School of Aeronautics and Astronautics, Shanghai Jiao Tong University, China. His research interests include astrodynamics, solar sail, electric sail, and spacecraft formation flying.

Von der hinteren Coverseite

The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining.  In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences.  This information has been of great interest for analyzing the sequential data to find its inherent characteristics.  Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces.

To meet the different needs of various applications, several models of sequential patterns have been proposed.   This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. 

Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable.  This book provides an efficient algorithm for mining these patterns.

Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry and also suitable for graduate-level students in computer science. 

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2010
• Sprache: Englisch
• ISBN 10: 1441937072
• ISBN 13: 9781441937070
• Einband: Taschenbuch
• Anzahl der Seiten: 180
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland