Mining Graph Data (eBook)

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

eBook Download: PDF

2006
John Wiley & Sons (Verlag)
978-0-470-07303-2 (ISBN)

Infos

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 minimal background in analyzing graph data, with this book you ll 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. There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.

Autor

DIANE J. COOK, PhD, is the Huie-Rogers Chair Professor in the School of Electrical Engineering and Computer Science at Washington State University. Her extensive research in artificial intelligence and data mining has been supported by grants from the National Science Foundation, NASA, DARPA, and Texas Instruments. Dr. Cook is the coauthor of Smart Environments: Technology, Protocols, and Applications (Wiley). LAWRENCE B. HOLDER, PhD, is Professor in the School of Electrical Engineering and Computer Science at Washington State University, where he teaches and conducts research in artificial intelligence, machine learning, data mining, graph theory, parallel and distributed processing, and cognitive architectures.

Rezensionen

"...individuals with no background analyzing graph data can learn how to represent the data as graphs, extract patterns or concepts from the data, and see how researchers apply the methodologies to real datasets." (Computing Reviews.com, March 23, 2007)

Produkt-Details

• Erscheint lt. Verlag: 4.1.2007
• Zusatzinfo: Drawings: 172 B&W, 0 Color; Screen captures: 17 B&W, 0 Color; Tables: 41 B&W, 0 Color
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Analysis
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: algorithmic • Applications • arise • challenges • Chapters • Comprehensive • Computer Science • Data • Data Mining • Data Mining Statistics • Discover • Electrical & Electronics Engineering • Elektrotechnik u. Elektronik • Field • Findings • Graph • Including • Informatik • Latest • leading • many • Minimal • Mining • Mustererkennung • Pattern Analysis • Practice • Represent • Researcher • Solutions • Statistics • Statistik • techniques • theory • Visualisierung u. Computergraphik • Visualization & Computer Graphics
• ISBN-10: 0-470-07303-9 / 0470073039
• ISBN-13: 978-0-470-07303-2 / 9780470073032