Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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EUR 115,42
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 115,79
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 118,83
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EUR 130,73
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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 495.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 141,80
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 134,61
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Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days.
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EUR 169,27
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Aggiungi al carrelloHardcover. Condizione: Brand New. 480 pages. 9.49x6.18x1.26 inches. In Stock.
Da: Majestic Books, Hounslow, Regno Unito
EUR 65,10
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 65,41
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Lingua: Inglese
Editore: Palgrave Macmillan, Springer, 2025
ISBN 10: 3031938011 ISBN 13: 9783031938016
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 42,79
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides a thorough introduction to graph mining and addresses foundational concepts and advanced techniques along with practical applications across various fields. As graphs have become increasingly vital for data representation in domains such as social network analysis, bioinformatics, and transportation, there is a growing demand for a comprehensive source that covers both theory and practical insights. This book seeks to fill that gap by offering clear explanations, practical examples, and actionable insights, making complex graph mining techniques accessible to students, postgraduate readers, and researchers. The authors also provide an extensive investigation into the process of gaining insightful knowledge from graph representations, ranging from interpreting intricate relationships to decoding complex data structures. Readers will learn to identify anomalous patterns, locate communities, arrange nodes, predict connections, and evaluate graphs effectively.
Da: preigu, Osnabrück, Germania
EUR 40,20
Quantità: 5 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Graph Mining | Practical Uses and Instruments for Exploring Complex Networks | Riju Bhattacharya (u. a.) | Buch | Synthesis Lectures on Computer Science | xv | Englisch | 2025 | Springer | EAN 9783031938016 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Da: Majestic Books, Hounslow, Regno Unito
EUR 121,12
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Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 118,84
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Aggiungi al carrelloHardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 133,26
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Aggiungi al carrelloHRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: moluna, Greven, Germania
EUR 99,37
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Nagiza F. Samatova is an associate professor of computer science at North Carolina State University and a senior research scientist at Oak Ridge National Laboratory.Discover Novel and Insightful Knowledge from Data Represented as a Gra.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 166,24
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Assuming no prior knowledge of mathematics or data mining, this self-contained book presents a 'do-it-yourself' approach to extracting interesting patterns from graph data. Each chapter focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through many applications, the book demonstrates how computational techniques can help solve real-world problems. Every algorithm and example is accompanied with R code, allowing readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice.