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Mining Graph Data by Diane J. Cook Hardcover | Indigo Chapters
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ISBN: 9780471731900

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have mi… Altro …

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Cook, Diane J. (Herausgeber); Holder, Lawrence B. (Herausgeber):

Mining Graph Data - copertina rigida, flessible

2006, ISBN: 0471731900

Gebundene Ausgabe Informationsrückgewinnung, Information Retrieval, Systemadministration, mit Schutzumschlag 11, [PU:Wiley-Interscience; John Wiley & Sons]

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Dj Cook:
Mining Graph Data - copertina rigida, flessible

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ISBN: 9780471731900

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Mining Graph Data - copertina rigida, flessible

2006, ISBN: 0471731900

[EAN: 9780471731900], Neubuch, [PU: Wiley-Interscience], New Copy. Customer Service Guaranteed, Books

NEW BOOK. Costi di spedizione: EUR 28.05 GoldBooks, Austin, TX, U.S.A. [71454205] [Rating: 5 (von 5)]
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Cook, DJ:
Mining Graph Data - copertina rigida, flessible

2006, ISBN: 0471731900

[EAN: 9780471731900], Neubuch, [PU: Narcissus.me], Books

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Mining Graph Data by Diane J. Cook Hardcover | Indigo Chapters

Discover the latest data mining techniques for analyzing graph data This text takes a focused and comprehensive look at an area of data mining that is quickly rising to the forefront of the field: mining data that is represented as a graph. Each chapter is written by a leading researcher in the field; collectively, the chapters represent the latest findings and applications in both theory and practice, including solutions to many of the algorithmic challenges that arise in mining graph data. Following the authors' step-by-step guidance, even readers with minimal background in analyzing graph data will be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. Mining Graph Data is divided into three parts: * Part I, Graphs, offers an introduction to basic graph terminology and techniques. * Part II, Mining Techniques, features a detailed examination of computational techniques for extracting patterns from graph data. These techniques are the state of the art in frequent substructure mining, link analysis, graph kernels, and graph grammars. * Part III, Applications, describes the application of data mining techniques to four graph-based application domains: chemical graphs, bioinformatics data, Web graphs, and social networks. Practical case studies are included in many of the chapters. An accompanying Web site features source code and datasets, offering readers the opportunity to experiment with the techniques presented in the book as well as test their own ideas on graph data. The Web site also includes the results of many of the techniques presented in the text. This landmark work is intended for students and researchers in computer science, information systems, and data mining who want to learn how to analyze and extract useful patterns and concepts from graph data.

Informazioni dettagliate del libro - Mining Graph Data by Diane J. Cook Hardcover | Indigo Chapters


EAN (ISBN-13): 9780471731900
ISBN (ISBN-10): 0471731900
Copertina rigida
Anno di pubblicazione: 2006
Editore: Diane J. Cook
500 Pagine
Peso: 0,839 kg
Lingua: eng/Englisch

Libro nella banca dati dal 2007-06-26T18:47:26+02:00 (Rome)
Pagina di dettaglio ultima modifica in 2023-07-26T10:51:27+02:00 (Rome)
ISBN/EAN: 0471731900

ISBN - Stili di scrittura alternativi:
0-471-73190-0, 978-0-471-73190-0
Stili di scrittura alternativi e concetti di ricerca simili:
Autore del libro : holder, lawrence cook, hölder, diane cook
Titolo del libro: mining graph data, data net ufo


Dati dell'editore

Autore: Diane J. Cook; Lawrence B. Holder
Titolo: Mining Graph Data
Editore: John Wiley & Sons
500 Pagine
Anno di pubblicazione: 2006-12-15
Peso: 0,834 kg
Lingua: Inglese
139,00 € (DE)
No longer receiving updates
165mm x 237mm x 29mm

BB; gebunden; Hardcover, Softcover / Technik/Elektronik, Elektrotechnik, Nachrichtentechnik; Elektronik, Nachrichtentechnik; Data Mining Statistics; Visualisierung u. Computergraphik; Computer Science; Mustererkennung; Statistics; Informatik; Data Mining; Data mining; Electrical & Electronics Engineering; Visualization & Computer Graphics; Pattern Analysis; Statistik; Elektrotechnik u. Elektronik; Data Mining; Visualisierung u. Computergraphik; Mustererkennung; Mustererkennung

Preface. Acknowledgments. Contributors. 1 INTRODUCTION (Lawrence B. Holder and Diane J. Cook). Part I GRAPHS. 2 GRAPH MATCHING--EXACT AND ERROR-TOLERANT METHODS AND THE AUTOMATIC LEARNING OF EDIT COSTS (Horst Bunke and Michel Neuhaus). 3 GRAPH VISUALIZATION AND DATA MINING (Walter Didimo and Giuseppe Liotta). 4 GRAPH PATTERNS AND THE R-MAT GENERATOR (Deepayan Chakrabarti and Christos Faloutsos). Part II MINING TECHNIQUES. 5 DISCOVERY OF FREQUENT SUBSTRUCTURES (Xifeng Yan and Jiawei Han). 6 FINDING TOPOLOGICAL FREQUENT PATTERNS FROM GRAPH DATASETS (Michihiro Kuramochi and George Karypis). 7 UNSUPERVISED AND SUPERVISED PATTERN LEARNING IN GRAPH DATA (Diane J. Cook, Lawrence B. Holder, and Nikhil Ketkar). 8 GRAPH GRAMMAR LEARNING (Istvan Jonyer). 9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio). 10 SOME LINKS BETWEEN FORMAL CONCEPT ANALYSIS AND GRAPH MINING (Michel Liquière). 11 KERNEL METHODS FOR GRAPHS (Thomas Gärtner, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel). 12 KERNELS AS LINK ANALYSIS MEASURES (Masashi Shimbo and Takahiko Ito). 13 ENTITY RESOLUTION IN GRAPHS (Indrajit Bhattacharya and Lise Getoor). Part III APPLICATIONS. 14 MINING FROM CHEMICAL GRAPHS (Takashi Okada). 15 UNIFIED APPROACH TO ROOTED TREE MINING: ALGORITHMS AND APPLICATIONS (Mohammed Zaki). 16 DENSE SUBGRAPH EXTRACTION (Andrew Tomkins and Ravi Kumar). 17 SOCIAL NETWORK ANALYSIS (Sherry E. Marcus, Melanie Moy, and Thayne Coffman). Index.

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