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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010139919 | QA76.9.D343 M56 2007 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Author Notes
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.
Table of Contents
| Preface |
| Acknowledgments |
| Contributors |
| 1 IntroductionLawrence B. Holder and Diane J. Cook |
| 1.1 Terminology |
| 1.2 Graph Databases |
| 1.3 Book Overview |
| References |
| Part I Graphs |
| 2 Graph Matching-Exact and Error-Tolerant Methods and the Automatic Learning of Edit CostsHorst Bunke and Michel Neuhaus |
| 2.1 Introduction |
| 2.2 Definitions and Graph Matching Methods |
| 2.3 Learning Edit Costs |
| 2.4 Experimental Evaluation |
| 2.5 Discussion and Conclusions |
| References |
| 3 Graph Visualization and Data MiningWalter Didimo and Giuseppe Liotta |
| 3.1 Introduction |
| 3.2 Graph Drawing Techniques |
| 3.3 Examples of Visualization Systems |
| 3.4 Conclusions |
| References |
| 4 Graph Patterns and the R-mat GeneratorDeepayan Chakrabarti and Christos Faloutsos |
| 4.1 Introduction |
| 4.2 Background and Related Work |
| 4.3 NetMine and R-MAT |
| 4.4 Experiments |
| 4.5 Conclusions |
| References |
| Part II Mining Techniques |
| 5 Discovery of Frequent SubstructuresXifeng Yan and Jiawei Han |
| 5.1 Introduction |
| 5.2 Preliminary Concepts |
| 5.3 Apriori-based Approach |
| 5.4 Pattern Growth Approach |
| 5.5 Variant Substructure Patterns |
| 5.6 Experiments and Performance Study |
| 5.7 Conclusions |
| References |
| 6 Finding Topological Frequent Patterns From Graph DatasetsMichihiro Kuramochi and George Karypis |
| 6.1 Introduction |
| 6.2 Background Definitions and Notation |
| 6.3 Frequent Pattern Discovery from Graph Datasets-Problem Definitions |
| 6.4 FSG for the Graph-Transaction Setting |
| 6.5 SIGRAM for the Single-Graph Setting |
| 6.6 GREW-Scalable Frequent Subgraph Discovery Algorithm |
| 6.7 Related Research |
| 6.8 Conclusions |
| References |
| 7 Unsupervised and Supervised Pattern Learning in Graph DataDiane J. Cook and Lawrence B. Holder and Nikhil Ketkar |
| 7.1 Introduction |
| 7.2 Mining Graph Data Using Subdue |
| 7.3 Comparison to Other Graph-Based Mining Algorithms |
| 7.4 Comparison to Frequent Substructure Mining Approaches |
| 7.5 Comparison to ILP Approaches |
| 7.6 Conclusions |
| References |
| 8 Graph Grammar LearningIstvan Jonyer |
| 8.1 Introduction |
| 8.2 Related Work |
| 8.3 Graph Grammar Learning |
| 8.4 Empirical Evaluation |
| 8.5 Conclusion |
| References |
| 9 Constructing Decision Tree Based on Chunkingless Graph-Based InductionKouzou Ohara and Phu Chien Nguyen and Akira Mogi and Hiroshi Motoda and Takashi Washio |
| 9.1 Introduction |
| 9.2 Graph-Based Induction Revisited |
| 9.3 Problem Caused by Chunking in B-GBI |
| 9.4 Chunkingless Graph-Based Induction (Cl-GBI) |
| 9.5 Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI) |
| 9.6 Conclusions |
| References |
| 10 Some links between formal concept analysis and graph miningMichel Liquiere |
| 10.1 Presentation |
| 10.2 Basic Concepts and Notation |
| 10.3 Formal Concept Analysis |
| 10.4 Extension Lattice and Description Lattice Give Concept Lattice |
| 10.5 Graph Description and Galois Lattice |
| 10.6 Graph Mining and Formal Propositionalization |
| 10.7 Conclusion |
| References |
| 11 Kernel Methods for GraphsThomas Gartner and Tamas Horvath and Quoc V. Le and Alex J. Smola and Stefan Wrobel |
| 11.1 Introduction |
| 11.2 Graph Classification |
| 11.3 Vertex Classification |
| 11.4 Conclusions and Future Work |
| References |
| 12 Kernels as Link Analysis MeasuresMasashi Sh |
