Borgelt C., Kruse R., Steinbrecher M. Graphical Models: Methods for Data Analysis and Mining

John Wiley & Sons, Ltd, 2009. — 393 p. — (Wiley Series in Computational Statistics). — ISBN: 978-0-470-72210-7.Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modeling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.Data and Knowledge
Knowledge Discovery and Data Mining
Graphical Models
Outline of this Book
Imprecision and Uncertainty
Modeling Inferences
Imprecision and Relational Algebra
Uncertainty and Probability Theory
Possibility Theory and the Context Model
Decomposition
Decomposition and Reasoning
Relational Decomposition
Probabilistic Decomposition
Possibilistic Decomposition
Possibility versus Probability
Graphical Representation
Conditional Independence Graphs
Evidence Propagation in Graphs
Computing Projections
Databases of Sample Cases
Relational and Sum Projections
Expectation Maximization
Maximum Projections
Naive Classifiers
Naive Bayes Classifiers
A Naive Possibilistic Classifier
Classifier Simplification
Experimental Evaluation
Learning Global Structure
Principles of Learning Global Structure
Evaluation Measures
Search Methods
Experimental Evaluation
Learning Local Structure
Local Network Structure
Learning Local Structure
Experimental Evaluation
Inductive Causation
Correlation and Causation
Causal and Probabilistic Structure
Faithfulness and Latent Variables
The Inductive Causation Algorithm
Critique of the Underlying Assumptions
Evaluation
Visualization
Potentials
Association Rules
Applications
Diagnosis of Electrical Circuits
Application in Telecommunications
Application at Volkswagen
Application at DaimlerChrysler
A Proofs of Theorems
B Software Tools