Record:   Prev Next
Author Schenker, Adam
Title Graph-Theoretic Techniques for Web Content Mining
Imprint Singapore : World Scientific Publishing Co Pte Ltd, 2005
©2005
book jacket
Descript 1 online resource (249 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Series in Machine Perception and Artificial Intelligence Ser. ; v.62
Series in Machine Perception and Artificial Intelligence Ser
Note Intro -- Preface -- Acknowledgements -- Contents -- Chapter 1 Introduction to Web Mining -- 1.1 Overview of Web Mining Methodologies -- 1.2 Traditional Information Retrieval Techniques -- 1.2.1 Vector-based distance measures -- 1.2.2 Special considerations for web documents -- 1.3 Overview of Remaining Chapters -- Chapter 2 Graph Similarity Techniques -- 2.1 Graph and Subgraph Isomorphism -- 2.2 Graph Edit Distance -- 2.3 Maximum Common Subgraph / Minimum Common Supergraph Approach -- 2.4 State Space Search Approach -- 2.5 Probabilistic Approach -- 2.6 Distance Preservation Approach -- 2.7 Relaxation Approaches -- 2.8 Mean and Median of Graphs -- 2.9 Summary -- Chapter 3 Graph Models for Web Documents -- 3.1 Pre-Processing -- 3.2 Graph Representations of Web Documents -- 3.3 Complexity Analysis -- 3.4 Web Document Data Sets -- Chapter 4 Graph-Based Clustering -- 4.1 The Graph-Based k-Means Clustering Algorithm -- 4.2 Clustering Performance Measures -- 4.3 Comparison with Previously Published Results -- 4.4 Comparison of Different Graph-Theoretical Distance Measures and Graph Representations for Graph-Based Clustering -- 4.4.1 Comparison of distance measures -- 4.4.2 Comparison of graph representations -- 4.5 Comparison of Clustering Algorithms -- 4.6 Visualization of Graph Clustering -- 4.7 The Graph-Based Global k-Means Algorithm -- 4.7.1 Global k-means vs. random initialization -- 4.7.2 Optimum number of clusters -- Chapter 5 Graph-Based Classification -- 5.1 The k-Nearest Neighbors Algorithm -- 5.1.1 Traditional method -- 5.1.2 Graph-based approach -- 5.1.3 Experimental results -- 5.2 Graph-Based Multiple Classifier Ensembles -- 5.2.1 Basic algorithm -- 5.2.2 Experimental results -- Chapter 6 The Graph Hierarchy Construction Algorithm for Web Search Clustering -- 6.1 Cluster Hierarchy Construction Algorithm (CHCA)
6.1.1 A review of inheritance -- 6.1.2 Brief overview of CHCA -- 6.1.3 CHCA in detail -- 6.1.4 CHCA: An example -- 6.1.5 Examination of CHCA as a clustering method -- 6.2 Application of CHCA to Search Results Processing -- 6.2.1 Asynchronous search -- 6.2.2 Implementation, input preparation and pre-processing -- 6.2.3 Selection of parameters for web search -- 6.3 Examples of Results -- 6.3.1 Comparison with Grouper -- 6.3.2 Comparison with Vivisimo -- 6.4 Graph Hierarchy Construction Algorithm (GHCA) -- 6.4.1 Parameters -- 6.4.2 Graph creation and pre-processing -- 6.4.3 Graph Hierarchy Construction Algorithm (GHCA) -- 6.4.4 GHCA examples -- 6.5 Comments -- Chapter 7 Conclusions and Future Work -- Appendix A Graph Examples -- College of Engineering -- Professional Engineering -- Professional Registration -- Preparation for Engineering -- Student Computer Policy -- Departments and Programs -- Chemical Engineering -- Civil and Environmental Engineering -- Computer Science and Engineering -- Electrical Engineering -- Industrial and Management Systems Engineering -- Mechanical Engineering -- Computer Service (SC) Courses -- College Computing Facilities -- Cooperative Education and Internship Programs -- Army, Air Force & Navy R.O.T.C. For Engineering Students -- Appendix B List of Stop Words -- Bibliography -- Index
Key Features:Opens up exciting new possibilities for utilizing graphs in common machine learning algorithmsPresents experimental results comparing differing graph representations and graph distance measuresProvides a review of graph-theoretic similarity techniques
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Schenker, Adam Graph-Theoretic Techniques for Web Content Mining Singapore : World Scientific Publishing Co Pte Ltd,c2005 9789812563392
Subject Data mining.;Graph theory -- Data processing.;Algorithms.;Multidimensional scaling
Electronic books
Alt Author Bunke, Horst
Last, Mark
Record:   Prev Next