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Mining graph data [[electronic resource] /] / edited by Diane J. Cook, Lawrence B. Holder
| Mining graph data [[electronic resource] /] / edited by Diane J. Cook, Lawrence B. Holder |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (501 p.) |
| Disciplina | 005.74 |
| Altri autori (Persone) |
CookDiane J. <1963->
HolderLawrence B. <1964-> |
| Soggetto topico |
Data mining
Data structures (Computer science) Graphic methods |
| ISBN |
1-280-74020-5
9786610740208 0-470-07304-7 0-470-07303-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
MINING GRAPH DATA; CONTENTS; Preface; Acknowledgments; Contributors; 1 INTRODUCTION; 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 COSTS; 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 MINING; 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 GENERATOR4.1 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 SUBSTRUCTURES; 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 DATASETS; 6.1 Introduction; 6.2 Background Definitions and Notation 6.3 Frequent Pattern Discovery from Graph Datasets-Problem Definitions6.4 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 DATA; 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 LEARNING; 8.1 Introduction 8.2 Related Work8.3 Graph Grammar Learning; 8.4 Empirical Evaluation; 8.5 Conclusion; References; 9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION; 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 MINING; 10.1 Presentation; 10.2 Basic Concepts and Notation; 10.3 Formal Concept Analysis 10.4 Extension Lattice and Description Lattice Give Concept Lattice10.5 Graph Description and Galois Lattice; 10.6 Graph Mining and Formal Propositionalization; 10.7 Conclusion; References; 11 KERNEL METHODS FOR GRAPHS; 11.1 Introduction; 11.2 Graph Classification; 11.3 Vertex Classification; 11.4 Conclusions and Future Work; References; 12 KERNELS AS LINK ANALYSIS MEASURES; 12.1 Introduction; 12.2 Preliminaries; 12.3 Kernel-based Unified Framework for Importance and Relatedness; 12.4 Laplacian Kernels as a Relatedness Measure; 12.5 Practical Issues; 12.6 Related Work 12.7 Evaluation with Bibliographic Citation Data |
| Record Nr. | UNINA-9910143402403321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mining graph data [[electronic resource] /] / edited by Diane J. Cook, Lawrence B. Holder
| Mining graph data [[electronic resource] /] / edited by Diane J. Cook, Lawrence B. Holder |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (501 p.) |
| Disciplina | 005.74 |
| Altri autori (Persone) |
CookDiane J. <1963->
HolderLawrence B. <1964-> |
| Soggetto topico |
Data mining
Data structures (Computer science) Graphic methods |
| ISBN |
1-280-74020-5
9786610740208 0-470-07304-7 0-470-07303-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
MINING GRAPH DATA; CONTENTS; Preface; Acknowledgments; Contributors; 1 INTRODUCTION; 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 COSTS; 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 MINING; 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 GENERATOR4.1 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 SUBSTRUCTURES; 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 DATASETS; 6.1 Introduction; 6.2 Background Definitions and Notation 6.3 Frequent Pattern Discovery from Graph Datasets-Problem Definitions6.4 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 DATA; 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 LEARNING; 8.1 Introduction 8.2 Related Work8.3 Graph Grammar Learning; 8.4 Empirical Evaluation; 8.5 Conclusion; References; 9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION; 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 MINING; 10.1 Presentation; 10.2 Basic Concepts and Notation; 10.3 Formal Concept Analysis 10.4 Extension Lattice and Description Lattice Give Concept Lattice10.5 Graph Description and Galois Lattice; 10.6 Graph Mining and Formal Propositionalization; 10.7 Conclusion; References; 11 KERNEL METHODS FOR GRAPHS; 11.1 Introduction; 11.2 Graph Classification; 11.3 Vertex Classification; 11.4 Conclusions and Future Work; References; 12 KERNELS AS LINK ANALYSIS MEASURES; 12.1 Introduction; 12.2 Preliminaries; 12.3 Kernel-based Unified Framework for Importance and Relatedness; 12.4 Laplacian Kernels as a Relatedness Measure; 12.5 Practical Issues; 12.6 Related Work 12.7 Evaluation with Bibliographic Citation Data |
| Record Nr. | UNINA-9910830998803321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mining graph data / / edited by Diane J. Cook, Lawrence B. Holder
| Mining graph data / / edited by Diane J. Cook, Lawrence B. Holder |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (501 p.) |
| Disciplina | 005.74 |
| Altri autori (Persone) |
CookDiane J. <1963->
HolderLawrence B. <1964-> |
| Soggetto topico |
Data mining
Data structures (Computer science) Graphic methods |
| ISBN |
9786610740208
9781280740206 1280740205 9780470073049 0470073047 9780470073032 0470073039 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
MINING GRAPH DATA; CONTENTS; Preface; Acknowledgments; Contributors; 1 INTRODUCTION; 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 COSTS; 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 MINING; 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 GENERATOR4.1 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 SUBSTRUCTURES; 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 DATASETS; 6.1 Introduction; 6.2 Background Definitions and Notation 6.3 Frequent Pattern Discovery from Graph Datasets-Problem Definitions6.4 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 DATA; 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 LEARNING; 8.1 Introduction 8.2 Related Work8.3 Graph Grammar Learning; 8.4 Empirical Evaluation; 8.5 Conclusion; References; 9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION; 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 MINING; 10.1 Presentation; 10.2 Basic Concepts and Notation; 10.3 Formal Concept Analysis 10.4 Extension Lattice and Description Lattice Give Concept Lattice10.5 Graph Description and Galois Lattice; 10.6 Graph Mining and Formal Propositionalization; 10.7 Conclusion; References; 11 KERNEL METHODS FOR GRAPHS; 11.1 Introduction; 11.2 Graph Classification; 11.3 Vertex Classification; 11.4 Conclusions and Future Work; References; 12 KERNELS AS LINK ANALYSIS MEASURES; 12.1 Introduction; 12.2 Preliminaries; 12.3 Kernel-based Unified Framework for Importance and Relatedness; 12.4 Laplacian Kernels as a Relatedness Measure; 12.5 Practical Issues; 12.6 Related Work 12.7 Evaluation with Bibliographic Citation Data |
| Record Nr. | UNINA-9911020006703321 |
| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||