Mining Graph Data

Diane J. Cook

ISBN 10: 0471731900 / ISBN 13: 9780471731900

Published by John Wiley & Sons Nov 2006, 2006

About this Item

Neuware - Discover the latest data mining techniques for analyzing graph dataThis 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.

Seller Inventory # 9780471731900

Bibliographic Details

• Title: Mining Graph Data
• Publisher: John Wiley & Sons Nov 2006
• Publication Date: 2006
• Language: English
• ISBN 10: 0471731900
• ISBN 13: 9780471731900
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 31D millimeters 240 millimeters width by 161 millimeters height by 31 millimeters depth
• Weight: 912 grams

About this title

Synopsis

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.

About the Author

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.