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Langue: anglais
Edité par Palgrave Macmillan, Springer, 2025
ISBN 10 : 3031938011 ISBN 13 : 9783031938016
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Ajouter au panierBuch. Etat : 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.
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Ajouter au panierBuch. Etat : 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.
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Ajouter au panierGebunden. Etat : 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.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierBuch. Etat : 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.