{"product_id":"mining-graph-data-hardback-9780471731900","title":"Mining Graph Data (Hardback) 9780471731900","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMining Graph Data\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eDiane J. Cook (Edited by), DJ Cook (Author), Lawrence B. Holder (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471731900, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 15 December 2006\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e512 pages, Drawings: 172 B\u0026amp;W, 0 Color; Screen captures: 17 B\u0026amp;W, 0 Color; Tables: 41 B\u0026amp;W, 0 Color\u003cbr\u003e24.1 x 17.5 x 3.6 cm, 0.885 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"…individuals with no background analyzing graph data can learn how to represent the data as graphs, extract patterns or concepts from the data, and see how researchers apply the methodologies to real datasets.\" (\u003ci\u003eComputing Reviews.com\u003c\/i\u003e, March 23, 2007)\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis 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.  \u003cp\u003eThere 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAcknowledgments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eContributors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1\u003c\/b\u003e \u003cb\u003eINTRODUCTION\u003c\/b\u003e (\u003ci\u003eLawrence B. Holder and Diane J. Cook\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e1.1 Terminology.\u003c\/p\u003e \u003cp\u003e1.2 Graph Databases.\u003c\/p\u003e \u003cp\u003e1.3 Book Overview.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I\u003c\/b\u003e \u003cb\u003eGRAPHS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2\u003c\/b\u003e \u003cb\u003eGRAPH MATCHING—EXACT AND ERROR-TOLERANT METHODS AND THE AUTOMATIC LEARNING OF EDIT COSTS\u003c\/b\u003e (\u003ci\u003eHorst Bunke and Michel Neuhaus\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Definitions and Graph Matching Methods.\u003c\/p\u003e \u003cp\u003e2.3 Learning Edit Costs.\u003c\/p\u003e \u003cp\u003e2.4 Experimental Evaluation.\u003c\/p\u003e \u003cp\u003e2.5 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3\u003c\/b\u003e \u003cb\u003eGRAPH VISUALIZATION AND DATA MINING\u003c\/b\u003e (\u003ci\u003eWalter Didimo and Giuseppe Liotta\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Graph Drawing Techniques.\u003c\/p\u003e \u003cp\u003e3.3 Examples of Visualization Systems.\u003c\/p\u003e \u003cp\u003e3.4 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4\u003c\/b\u003e \u003cb\u003eGRAPH PATTERNS AND THE R-MAT GENERATOR\u003c\/b\u003e (\u003ci\u003eDeepayan Chakrabarti and Christos Faloutsos\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Background and Related Work.\u003c\/p\u003e \u003cp\u003e4.3 NetMine and R-MAT.\u003c\/p\u003e \u003cp\u003e4.4 Experiments.\u003c\/p\u003e \u003cp\u003e4.5 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II\u003c\/b\u003e \u003cb\u003eMINING TECHNIQUES.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5\u003c\/b\u003e \u003cb\u003eDISCOVERY OF FREQUENT SUBSTRUCTURES\u003c\/b\u003e (\u003ci\u003eXifeng Yan and Jiawei Han\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Preliminary Concepts.\u003c\/p\u003e \u003cp\u003e5.3 Apriori-based Approach.\u003c\/p\u003e \u003cp\u003e5.4 Pattern Growth Approach.\u003c\/p\u003e \u003cp\u003e5.5 Variant Substructure Patterns.\u003c\/p\u003e \u003cp\u003e5.6 Experiments and Performance Study.\u003c\/p\u003e \u003cp\u003e5.7 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6\u003c\/b\u003e \u003cb\u003eFINDING TOPOLOGICAL FREQUENT PATTERNS FROM GRAPH DATASETS\u003c\/b\u003e (\u003ci\u003eMichihiro Kuramochi and George Karypis\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Background Definitions and Notation.\u003c\/p\u003e \u003cp\u003e6.3 Frequent Pattern Discovery from Graph Datasets—Problem Definitions.\u003c\/p\u003e \u003cp\u003e6.4 FSG for the Graph-Transaction Setting.\u003c\/p\u003e \u003cp\u003e6.5 SIGRAM for the Single-Graph Setting.\u003c\/p\u003e \u003cp\u003e6.6 GREW—Scalable Frequent Subgraph Discovery Algorithm.\u003c\/p\u003e \u003cp\u003e6.7 Related Research.\u003c\/p\u003e \u003cp\u003e6.8 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7\u003c\/b\u003e \u003cb\u003eUNSUPERVISED AND SUPERVISED PATTERN LEARNING IN GRAPH DATA\u003c\/b\u003e (\u003ci\u003eDiane J. Cook, Lawrence B. Holder, and Nikhil Ketkar\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Mining Graph Data Using Subdue.\u003c\/p\u003e \u003cp\u003e7.3 Comparison to Other Graph-Based Mining Algorithms.\u003c\/p\u003e \u003cp\u003e7.4 Comparison to Frequent Substructure Mining Approaches.\u003c\/p\u003e \u003cp\u003e7.5 Comparison to ILP Approaches.\u003c\/p\u003e \u003cp\u003e7.6 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8\u003c\/b\u003e \u003cb\u003eGRAPH GRAMMAR LEARNING\u003c\/b\u003e (\u003ci\u003eIstvan Jonyer\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Related Work.\u003c\/p\u003e \u003cp\u003e8.3 Graph Grammar Learning.\u003c\/p\u003e \u003cp\u003e8.4 Empirical Evaluation.\u003c\/p\u003e \u003cp\u003e8.5 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9\u003c\/b\u003e \u003cb\u003eCONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION\u003c\/b\u003e (\u003ci\u003eKouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Graph-Based Induction Revisited.\u003c\/p\u003e \u003cp\u003e9.3 Problem Caused by Chunking in B-GBI.\u003c\/p\u003e \u003cp\u003e9.4 Chunkingless Graph-Based Induction (Cl-GBI).\u003c\/p\u003e \u003cp\u003e9.5 Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI).\u003c\/p\u003e \u003cp\u003e9.6 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10\u003c\/b\u003e \u003cb\u003eSOME LINKS BETWEEN FORMAL CONCEPT ANALYSIS AND GRAPH MINING\u003c\/b\u003e (\u003ci\u003eMichel Liquière\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e10.1 Presentation.\u003c\/p\u003e \u003cp\u003e10.2 Basic Concepts and Notation.\u003c\/p\u003e \u003cp\u003e10.3 Formal Concept Analysis.\u003c\/p\u003e \u003cp\u003e10.4 Extension Lattice and Description Lattice Give Concept Lattice.\u003c\/p\u003e \u003cp\u003e10.5 Graph Description and Galois Lattice.\u003c\/p\u003e \u003cp\u003e10.6 Graph Mining and Formal Propositionalization.\u003c\/p\u003e \u003cp\u003e10.7 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11\u003c\/b\u003e \u003cb\u003eKERNEL METHODS FOR GRAPHS\u003c\/b\u003e (\u003ci\u003eThomas Gärtner, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Graph Classification.\u003c\/p\u003e \u003cp\u003e11.3 Vertex Classification.\u003c\/p\u003e \u003cp\u003e11.4 Conclusions and Future Work.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12\u003c\/b\u003e \u003cb\u003eKERNELS AS LINK ANALYSIS MEASURES\u003c\/b\u003e (\u003ci\u003eMasashi Shimbo and Takahiko Ito\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 Preliminaries.\u003c\/p\u003e \u003cp\u003e12.3 Kernel-based Unified Framework for Importance and Relatedness.\u003c\/p\u003e \u003cp\u003e12.4 Laplacian Kernels as a Relatedness Measure.\u003c\/p\u003e \u003cp\u003e12.5 Practical Issues.\u003c\/p\u003e \u003cp\u003e12.6 Related Work.\u003c\/p\u003e \u003cp\u003e12.7 Evaluation with Bibliographic Citation Data.\u003c\/p\u003e \u003cp\u003e12.8 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13\u003c\/b\u003e \u003cb\u003eENTITY RESOLUTION IN GRAPHS\u003c\/b\u003e (\u003ci\u003eIndrajit Bhattacharya and Lise Getoor\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Related Work.\u003c\/p\u003e \u003cp\u003e13.3 Motivating Example for Graph-Based Entity Resolution.\u003c\/p\u003e \u003cp\u003e13.4 Graph-Based Entity Resolution: Problem Formulation.\u003c\/p\u003e \u003cp\u003e13.5 Similarity Measures for Entity Resolution.\u003c\/p\u003e \u003cp\u003e13.6 Graph-Based Clustering for Entity Resolution.\u003c\/p\u003e \u003cp\u003e13.7 Experimental Evaluation.\u003c\/p\u003e \u003cp\u003e13.8 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III\u003c\/b\u003e \u003cb\u003eAPPLICATIONS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14\u003c\/b\u003e \u003cb\u003eMINING FROM CHEMICAL GRAPHS\u003c\/b\u003e (\u003ci\u003eTakashi Okada\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e14.1 Introduction and Representation of Molecules.\u003c\/p\u003e \u003cp\u003e14.2 Issues for Mining.\u003c\/p\u003e \u003cp\u003e14.3 CASE: A Prototype Mining System in Chemistry.\u003c\/p\u003e \u003cp\u003e14.4 Quantitative Estimation Using Graph Mining.\u003c\/p\u003e \u003cp\u003e14.5 Extension of Linear Fragments to Graphs.\u003c\/p\u003e \u003cp\u003e14.6 Combination of Conditions.\u003c\/p\u003e \u003cp\u003e14.7 Concluding Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15\u003c\/b\u003e \u003cb\u003eUNIFIED APPROACH TO ROOTED TREE MINING: ALGORITHMS AND APPLICATIONS\u003c\/b\u003e (\u003ci\u003eMohammed Zaki\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e15.1 Introduction.\u003c\/p\u003e \u003cp\u003e15.2 Preliminaries.\u003c\/p\u003e \u003cp\u003e15.3 Related Work.\u003c\/p\u003e \u003cp\u003e15.4 Generating Candidate Subtrees.\u003c\/p\u003e \u003cp\u003e15.5 Frequency Computation.\u003c\/p\u003e \u003cp\u003e15.6 Counting Distinct Occurrences.\u003c\/p\u003e \u003cp\u003e15.7 The SLEUTH Algorithm.\u003c\/p\u003e \u003cp\u003e15.8 Experimental Results.\u003c\/p\u003e \u003cp\u003e15.9 Tree Mining Applications in Bioinformatics.\u003c\/p\u003e \u003cp\u003e15.10 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16\u003c\/b\u003e \u003cb\u003eDENSE SUBGRAPH EXTRACTION\u003c\/b\u003e (\u003ci\u003eAndrew Tomkins and Ravi Kumar\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e16.1 Introduction.\u003c\/p\u003e \u003cp\u003e16.2 Related Work.\u003c\/p\u003e \u003cp\u003e16.3 Finding the densest subgraph.\u003c\/p\u003e \u003cp\u003e16.4 Trawling.\u003c\/p\u003e \u003cp\u003e16.5 Graph Shingling.\u003c\/p\u003e \u003cp\u003e16.6 Connection Subgraphs.\u003c\/p\u003e \u003cp\u003e16.7 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17\u003c\/b\u003e \u003cb\u003eSOCIAL NETWORK ANALYSIS\u003c\/b\u003e (\u003ci\u003eSherry E. Marcus, Melanie Moy, and Thayne Coffman\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e17.1 Introduction.\u003c\/p\u003e \u003cp\u003e17.2 Social Network Analysis.\u003c\/p\u003e \u003cp\u003e17.3 Group Detection.\u003c\/p\u003e \u003cp\u003e17.4 Terrorist Modus Operandi Detection System.\u003c\/p\u003e \u003cp\u003e17.5 Computational Experiments.\u003c\/p\u003e \u003cp\u003e17.6 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand New","offer_id":52298045980952,"sku":"9780471731900","price":97.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471731900.jpg?v=1781732523","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/mining-graph-data-hardback-9780471731900","provider":"Freshly Printed Books","version":"1.0","type":"link"}