LEADER 05390nam 22007094a 450 001 9911020006703321 005 20200520144314.0 010 $a9786610740208 010 $a9781280740206 010 $a1280740205 010 $a9780470073049 010 $a0470073047 010 $a9780470073032 010 $a0470073039 035 $a(CKB)1000000000355097 035 $a(EBL)284362 035 $a(OCoLC)437176195 035 $a(SSID)ssj0000203339 035 $a(PQKBManifestationID)11174117 035 $a(PQKBTitleCode)TC0000203339 035 $a(PQKBWorkID)10258874 035 $a(PQKB)11441425 035 $a(MiAaPQ)EBC284362 035 $a(PPN)185060625 035 $a(Perlego)2771486 035 $a(EXLCZ)991000000000355097 100 $a20060413d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMining graph data /$fedited by Diane J. Cook, Lawrence B. Holder 210 $aHoboken, N.J. $cWiley-Interscience$dc2007 215 $a1 online resource (501 p.) 300 $aDescription based upon print version of record. 311 08$a9780471731900 311 08$a0471731900 320 $aIncludes bibliographical references and index. 327 $aMINING 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 327 $a4 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 327 $a6.3 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 327 $a8.2 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 327 $a10.4 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 327 $a12.7 Evaluation with Bibliographic Citation Data 330 $aThis 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 606 $aData mining 606 $aData structures (Computer science) 606 $aGraphic methods 615 0$aData mining. 615 0$aData structures (Computer science) 615 0$aGraphic methods. 676 $a005.74 701 $aCook$b Diane J.$f1963-$01621465 701 $aHolder$b Lawrence B.$f1964-$01838904 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020006703321 996 $aMining graph data$94417999 997 $aUNINA