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Mining Graph Data Hardcover – November 28, 2006

ISBN-13: 978-0471731900 ISBN-10: 0471731900 Edition: 1st

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Product Details

  • Hardcover: 500 pages
  • Publisher: Wiley-Interscience; 1 edition (November 28, 2006)
  • Language: English
  • ISBN-10: 0471731900
  • ISBN-13: 978-0471731900
  • Product Dimensions: 6.9 x 1.4 x 9.5 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #1,470,502 in Books (See Top 100 in Books)

Editorial Reviews

Review

"…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)

From the Back Cover

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.

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Most Helpful Customer Reviews

6 of 9 people found the following review helpful By J. Chan on July 1, 2007
Format: Hardcover
This book provides a great survey of some recent advances in graph mining. When I started studying graph mining a few years back, there was no single book or reference to get a good introduction to the field. This book would have saved me some time in getting a firm grasp on the issues in graph mining.

The book provides the reader with a good introduction to the issues and different areas in graph mining. However, it does assume some familarity with graphs, and probably targeted at the level of postgraduate students. In addition, do not expect to obtain a thorough understanding of the field just from reading the book - its aim is to provide breadth, not to be a reference. Also, most of the contributions in the book is published work in conferences and journals, so can be obtained "free", if you organisation/institution you work at have access to the relevant publishers. Saying all that, I thorough recommend it if you are starting off in the field of graph mining, or just glad to be finally owning a graph mining book (like myself).
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