Graphical Models: Methods for Data Analysis and Mining [Hardcover]

Christian Borgelt (Author), Rudolf Kruse (Author)

Book Description

ISBN-10: 0470843373 | ISBN-13: 978-0470843376 | Publication Date: March 15, 2002 | Edition: 1

The concept of modelling using graph theory has its origin in several scientific areas, notably statistics, physics, genetics, and engineering. The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and is the first to include detailed coverage of possibilistic networks - a relatively new reasoning tool that allows the user to infer results from problems with imprecise data. One major advantage of graphical modelling is that specialised techniques that have been developed in one field can be transferred into others easily. The methods described here are applied in a number of industries, including a recent quality testing programme at a major car manufacturer.
* Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data
* Each concept is carefully explained and illustrated by examples
* Contains all necessary background, including modeling under uncertainty, decomposition of distributions, and graphical representation of decompositions
* Features applications of learning graphical models from data, and problems for further research
* Includes a comprehensive bibliography
An essential reference for graduate students of graphical modelling, applied statistics, computer science and engineering, as well as researchers and practitioners who use graphical models in their work.

Editorial Reviews

Review

"...positioned at the boundary between two highly important research areas...not restricted to probabilistic models..." (Zentralblatt Math, 2003)

"...a good and interesting book...every effort is made to make the concepts meaningful to the reader..." (Statistics in Medicine, Vol 23(11), 15 June 2004)

From the Back Cover

The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a tool that allows the user to infer results from problems with imprecise data.

One of the most important applications of graphical modelling today is data mining - the data-driven discovery and modelling of hidden patterns in large data sets. The techniques described have a wide range of industrial applications, and a quality testing programme at a major car manufacturer is included as a real-life example.
* Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data.

* Each concept is carefully explained and illustrated by examples.

* Contains all necessary background material, including modelling under uncertainty, decomposition of distributions, and graphical representation of decompositions.

* Features applications of learning graphical models from data, and problems for further research.

* Includes a comprehensive bibliography.
Graphical Models: Methods for Data Analysis and Mining will be invaluable to researchers and practitioners who use graphical models in their work. Graduate students of applied statistics, computer science and engineering will find this book provides an excellent introduction to the subject.

Product Details

• Hardcover: 368 pages
• Publisher: Wiley; 1 edition (March 15, 2002)
• Language: English
• ISBN-10: 0470843373
• ISBN-13: 978-0470843376
• Product Dimensions: 6.3 x 1 x 9.2 inches
• Shipping Weight: 1.5 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #4,055,245 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

11 of 13 people found the following review helpful:

4.0 out of 5 stars Good introduction, however focus on possibility, April 28, 2004

By A Customer

This review is from: Graphical Models: Methods for Data Analysis and Mining (Hardcover)

The book gives a good, very deep introduction to the topic of Graphical models and data mining. The main focus is on the data mining section, thus the reader should have a basic knowledge about the graphical model concept. It is certainly not a beginner's book or a tutorial on graphical models or Bayesian networks. Furthermore the book is very mathematical with quite a lot of definitions, lemmas and proofs. A good knowledge in set theory is mandatory. However, the theory is very well explained and illustrated with simple examples.

At some points I would have been more interested in more practical issues, however this may be an engineers view. From my point of view, the main drawback of the book is the strong focus on possibility theory.

However, I highly recommend this book for everybody interested in Graphical Models and especially in reasoning with possibility theory instead of probability theory. The reader should bring a good mathematical background. Then the book does not only provide good examples, but a knowledge based on a strong mathematical formalism. This allows the reader to fully understand the topic. Reading this book takes time and a lot of effort, but you can certainly benefit more from it than from most other books about this topic.